fabiochiu/medium-articles huggingface dataset:
Why watermelons are a symbol of Palestinian political protest
By: The Palestine Project
Tags: Military Occupation, Palestine, Israel, Intifada

In the years following the Six Day War in 1967, it became a crime to raise a Palestinian flag in Israeli-controlled Gaza and the West Bank. To subvert the ban on the national colors, it is said that Palestinians would carry sliced watermelons through the region in a sign of protest. In October of 1993, just weeks after Israel and the Palestinian Liberation Organization signed the Oslo Accords in Washington, DC, The New York Times reported that young men were once detained for holding watermelon halves in the Gaza Strip. An editor’s note several months later stated that the paper could not ultimately confirm the claims, but that an Israeli government official also could not deny that such arrests had taken place.

Similarly, prominent Palestinian artists have stated over the years that they risked imprisonment for simply painting depictions of watermelons, as well as other images (like poppy seed plants) that might have incorporated the national colors. Australia’s daily newspaper, The Age, also reported that the watermelon was used as a symbol of the Second Intifada — or Palestinian uprising — in 2000.

“The Israelis were extremely harsh in cracking down on any manifestation of Palestinian nationalism, and the more symbolic and the more visible, the angrier they would get,” explains Rashid Khalidi, the Edward Said Professor of Arab Studies at Columbia University and the editor of the Journal of Palestine Studies. “Palestine didn’t exist, the Palestinians didn’t exist, and anybody who claimed otherwise was in trouble.”

With tensions heightened — and an Israeli anti-terrorism bill designed to ban the Palestinian flag less than a year in the past — it remains unclear if the red, black, green, and white of the watermelon will once again become an image of political subversion in the Middle East.

“It’s a symbol of the existence of a national group and sort of the most striking,” Khalidi says of the importance of the Palestinian colors. “I mean, how could you express your Palestinian identity more visibly than by raising or waiving or painting a flag?”
You never knew the Indian RAW and Israeli Mossad were this close!
By: Vikyath Kumar
Tags: Mossad, India, Research And Analysis, Israel

The relationship of RAW and Mossad reached a greater height when Ajit Doval was appointed as the National Security Advisor for India.

If you have closely watched the Facebook live video of Israeli PM Benjamin Netanyahu during Modi’s visit to Israel, when Bibi introduced Yossi Cohen, their NSA who is also the current chief of Mossad, I remember PM Modi telling “Doval is in close contact with your NSA”.

While going through the news, I read the 7 agreements India and Israel singed during Modi’s visit and one among them was Intelligence Sharing.

The RAW and Mossad have same motto that is zero tolerance towards terrorism and the Indian intelligence promised to share any kind of threat to Israel from Asia and Mossad promised the same from Middle East.

The bromance of Mossad and RAW started in early 70’s when India was under Operation Kahuta where it spied the Pakistani Nuclear base but all thanks to Morarji Desai, a retard PM who destroyed the game which eventually resulted in assassination of all undercover agents in Pak.

It is also a well know news that during PM Modi’s G20 summit visit in Turkey, the RAW had gathered intelligence of a probable attack on Modi and many other world leaders. The Indian intelligence agency RAW managed to gain help from British intelligence MI6 and Mossad for the complete security of Narendra Modi till he landed back in India.

Ajit Doval and Yossi Cohen. Real life James Bonds :)

Footnotes,

Mossad, MI5 roped in to shield Prime Minister Narendra Modi in Turkey?

India-Israel intelligence co-operation
約翰藍儂的歌聲在以色列迴盪著
By: Eric Hung
Tags: 以色列, Beatles, Israel, 披頭四, John Lennon

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
One Medium writer you might want to follow is my good friend Elke Weiss
By: Rebecca Sealfon
Tags: Writers On Medium, Depression, Quora, Judaism, Israel

One Medium writer you might want to follow is my good friend Elke Weiss, who happens to be one of the most popular writers on Quora. On Medium, this very brave woman writes about her personal emotional struggles and really bares herself. She also writes about her attachment to Israel, her rejection of Orthodox Judaism, and her adventures learning Krav Maga. You’ll learn a lot.

Photo by Alexandru Acea on Unsplash
My Israel-Palestine forum Unity is Strength became the first Space on Quora.com to receive a grant from an external organization. The grant came from Sustainable Israel-Palestine Projects (SIPP). Now…
By: Rebecca Sealfon
Tags: Peacebuilding, Public Speaking, Israel, Funding, Palestine

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
Israel: Insurance company demands 50 BTC as ransom for hackers
Tags: Btc, Hacker, Israel

Bitcoin’s price, on the one hand, proves that the cryptocurrency is a great investment asset, while technological and Sunday advances can be of the wrong kind of mind, especially when Bitcoin has attracted the attention of hackers demanding ransom. It comes after popular insurance company Shirbit was hacked by hackers days earlier.



According to a local report, the hackers demanded a ransom of 50 BTC against the company’s threat to sell all data associated with its customers. The request was made by hackers with a detailed message on Telegram.



The group, identified as Black Shadow, requested that 50 BTC (equivalent to $ 1 million) be sent to a BTC address within 24 hours. The group also talked about doubling the ransom if the company did not pay the amount within the specified period. In fact, the hackers threatened to leak the aforementioned data if the ransom was not paid within the specified time.



Cybersecurity firm Hudson Rock was one of those to share a screenshot of the same on Twitter.



The car insurance company has many public service employees in Israel, and hackers have reportedly managed to get their hands on all its databases.



Company revealed”Tonight we received a million-dollar ransom demand along with an ultimatum. Along with the blackmail letter, the attackers leaked details about the insurance claims of several of our clients. The team of experts, among other agencies, is investigating the consequences of the message, and at the same time the company is pr
Official visit to Egypt by Netanyahu Plans
By: Akshay Prasad
Tags: Netanyahu, Egypt, Israel

Israeli Prime Minister Benjamin Netanyahu is reportedly preparing an official visit to Egypt, according to Maariv. Last week, Netanyahu went on an unannounced trip to Saudi Arabia and met Mohamed Bin Salman. Netanyahu will meet with Egyptian President Abdel Fattah al-Sisi in the coming weeks, according to sources, as part of efforts to strengthen the political and economic relations between the two countries. Meetings are arranged for delegations from both nations.

According to reports, the Israeli prime minister does not want the presence of new Israeli agreements with the UAE, Bahrain, and Sudan to have a negative effect on ties between Israel and Egypt. Netanyahu would aim to convince the Egyptian leadership that Egypt’s relations will not be compromised by Tel Aviv’s new relationship with the Arab world. To begin a new normalisation, he wants to speak to Sisi about improving ties between the two countries.

The key aim of the visit of Netanyahu to Egypt is to examine the Egyptian-Jordanian-Palestinian relations with a view to advancing the peace process and the overall US policy stance. He is also working with his Egyptian counterpart to work to create a new basis for Palestinian ties to be strengthened.

Having agreed on the basis of the agreement, Sisi will mediate with Palestinian President Mahmoud Abbas and the leaders of Isreal. Netanyahu also seeks to establish solidarity in the Middle East in order to enhance US cooperation, to mediate the position of the United States in the region and to strengthen regional allies.

Israel is negotiating deals with Arab countries to normalise relations. The agreement, brokered by Trump, has strengthened the political ties between Israel and the United Arab Emirates, Bahrain and Sudan, and now appears to be pursuing a diplomatic rapprochement with Cairo. Involved in a historic battle, Egypt and Israel seem to be moving closer to a period of improving relations between the Arab world and Israel.

Egyptian President Abdel Fattah Al-Sisi, meanwhile has been hesitant to reveal Israeli Prime Minister Benjamin Netanyahu’s scheduled official visit to Egypt, fearing public uproar and reaction. Diplomatic sources told news agencies that Cairo had purposely stopped confirming or denying rumours of a proposed visit by Netanyahu to Egypt. Other Israeli media narrowly published the news of Netanyahu’s visit.

“there are disagreements over this issue between Sisi and Netanyahu and their agencies over the possibility of formally announcing the existence of the visit.”there are disagreements between Sisi and Netanyahu and their agencies on the possibility of officially announcing the existence of the visit on this subject.

Netanyahu is seeking to present this to a regional government such as Egypt, following Donald Trump’s suggestion that Israel and Saudi Arabia sign a normalisation agreement after the US election. Egypt was the first country to do so in 1979, followed by Jordan, which signed a peace agreement in 1994.
Scratch ile Logistic Regression
By: Akif Emre Çapoğlu
Tags: Logistic Regression, Scratch, Deep Learning, Neural Networks, Machine Learning

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Logistic Regression
By: Arbaj Khan
Tags: Logistic Regression

Understanding the Logistic Regression

Regression analysis is a set of statistical methods used to estimate the relationships between a dependent variable and one or more independent variables. It can be used to assess the strength of the relationship between variables and to model the future relationship between them. There are many different regression models but linear and logistic regression is the most basic type of regression algorithm. The regression model is built upon the relationship between the dependent and independent variables in the dataset.

Introduction:

In this blog, we will discuss about the basic concepts of Logistic Regression. The logistic regression machine learning method is one of the simplest and simplest algorithms for sorting problems. Some examples of online trading fraud classification are malignant tumors and email spam classification. In logistic retrieval, the sigmoid logistic function is used to modify the output to return a probability value. Thus, the logistic return algorithm can be said to be a predictive analysis algorithm based on the concept of probability. Logistic retrieval is used to find the outcome of an event in binary values that may represent true or false. The result can be classified as 0 or 1, which represents failure or success, respectively.

Logistic Regression:

It is used for the classification problems, it can predictive analysis algorithm and based on the concept of probability.

logistic regression is statistical technique for analyzing a data set which can predicts the probability of an outcome that can only have two values.

Types of Logistic Regression:

Binary logistic regression

Binary logistics regression is used to predict the potential of being values on independent variables.

Multinomial logistic regression

It is used to guess the nominal dependent variable given one or more independent variables.

Advantages of Logistic Regression:

The probability of an event occurring depending on the values of the independent variables, which can be categorical or numerical.

It will estimate the probability that an event occurs for a randomly selected observation against the probability that the event does not occur.

predict the effect of the series of variables on a binary response variable.

Limitations:

Binary data does not have a normal distribution, which is a condition needed for most other types of regression.

Predict values of dependent variable can be beyond 0 an 1,which violates the definition of probability.

In logistic regression the predicted values has to be between 0 to 1.

Conclusion:

Logistic regression is a powerful tool, especially in epidemiological studies, allowing the simultaneous study of several interpretive variables, while minimizing the impact of confounders. However, researchers need to pay attention to the construction of the model, avoid feeding the software with only raw data and continue with the results. Some difficult decisions in construction models will depend entirely on the researcher’s experience in the field.
Challenger shuttle disaster: Predicting O-ring failure using Regression Model
Tags: Logistic Regression, NASA, Data Modeling, Predictive Modeling, Machine Learning

Motivation

On January 28, 1986, the Space Shuttle Challenger (OV-099) broke apart 73 seconds into its flight, resulting in catastrophe of the highest order — loss of life all seven crew members aboard.

The investigation reveal the root cause of this tragic accident to be a failure of o-ring seal in the shuttle’s right solid-fuel rocket booster. This seal was designed to prevent leaks from the fuel tank, however during liftoff the seal weakened at the frigid temperatures and failed, this causing hot gas to leak our of fuel tank. The fuel tank itself collapsed and tore apart, and the resulting flood of liquid oxygen and hydrogen created the huge fireball believed by many to be an explosion.

Problem Statement:

Predict the number of O-rings that experience thermal distress on a flight at 31 degrees F given data on the previous 23 shuttle flights

Attribute Information:

1. Number of O-rings at risk on a given flight

2. Number experiencing thermal distress

3. Launch temperature (degrees F)

4. Leak-check pressure (psi)

5. Temporal order of flight

Dateset source can be found here and here

Data cleaning/Preparation

Let’s look at the dataset:

The given dataset doesn’t have any missing values, further we’ll drop columns which have non-unique values, to get final data-frame:

Our target variable is RingsInDistress, which is number of rings failed (0,1,2)

Exploratory data analysis

Let’s look at distribution plot for all features:

Plot for RingInDistress versus Temperature and Pressure below:

Let’s see the trend among features using pair-plot:

pair plots for dependent features

We can further explore the correlations between features as heatmaps:

heat map

While RingsInDistress is strongly correlated to Launch Temperature and Leak-Check Pressure. There is less correlation between temperature and pressure themselves.

Predictions Using the Logistic Regression

After separating training set and test set. We have run MinMaxScaler to normalize the dependent feature datasets. Following which we’ll use Binomial Regression:

Linear Regression stats report

Let us find optimum cutoff probability by plotting sensitivity, accuracy and precision

plot sensitivity, accuracy and precision

as can be seen optimal cutoff is somewhere around 0.2.

With this information, we can make predictions on our test set as below:

Conclusion:

With logistic regression we can predict o-ring failure by 71.4%

In next segment, we’ll discuss how this can be further improved.

My work book version can be found here where I have explore prediction with Linear Regression and Logistic Regression
¿Quiénes votaron la IVE?
By: Adriana Chazarreta
Tags: Logistic Regression, Vote, Computational Sociology, Sociology, Argentina

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
Diving Deep Into Logistic Regression
By: Sharath H N
Tags: Machine Learning, Ml Unravelled, Logistic Regression

Recap

Logistic Regression is a statistical method used to predict the value of a binary dependent variable when a set of independent values are given. This is done by formulating a hypothesis by utilizing a previously available set of independent values and the corresponding value of the binary dependent variable. In this article, we will be looking at the detailed process of arriving at the hypothesis for data.

What Is The Final Goal?

This algorithm intends to fit a boundary, which is the graphical representation of the hypothesis so that all the values above it belong to the same class and differ from all the other ones on the other side of the boundary. It should be noted that the boundary need not be linear. Here are some examples showing the boundaries of various shapes.

Source: GFG9

Formulating The Hypothesis

Let us consider a hypothetical dataset with two independent variables and a dependent variable. Let X₁ and X₂ be two independent variables upon which the binary dependent variable Y depends. Just like in linear regression, we calculate the hypothesis by using randomly assigned weights θ₀, θ₁, and θ₂. But the formula for calculating hypothesis is light different than that of Linear Regression.

Z=θ₀ +X₁*θ₁+X₂*θ₂

h(X)=Sigmoid(Z)

Sigmoid Function

The sigmoid function is given by

Sigmoid(Z)= 1/(1+e^-Z) and the beauty of this function is, no matter what the value of Z is, sigmoid(Z) always falls in the range (0,1), which is exactly what we want. If you observe, the binary dependent variables take value 1 for positive class, 0 for negative class and the predicted hypothesis also lies in the comparable range.

Modified Cost Function

Here we are modifying the cost function to bring in convexity to the cost function, which is important to avoid local minima while applying Gradient Descent.

Gradient Descent

We run the above equation for a fixed number of iterations, called epochs, till the cost function minimizes and store the weights for further use.

Summarizing The Steps

Repeat

Calculate X*θ to find Z

Calculate Sigmoid(Z), which is the hypothesis.

Calculate the cost function.

Calculate new values of weights by using the differential of the cost function.

End the function once you are convinced with the minimized value.

In the next article, we will try to implement this algorithm on a simple dataset so that you can understand it’s working properly.

Stay Tuned!
Is logistic regression a good analysis tool for differential gene expression? Only PCR will tell.
By: Patricia Mason
Tags: Statistical Analysis, Logistic Regression, Bioinformatics, Bioinformatics Tools

As a biologist, I have never really thought out the computational resources I have used to analyze my data. The NIH National Center for Biotechnology (NCBI) website has always provided a fabulous resource for genomics and never disappoints. There are so many great tools for biologists as well as a repository for both uploading and downloading sequences or microarray data. But what about the code and statistical methods used for data analysis?

Bioconductor is an open-sourced software used for bioinformatics. Within Bioconductor is the package, limma. This package is the R code used to analyze microarray data on the Gene Expression Ominbus, GEO, website within the NCBI web site. Limma uses logistic regression combined with empirical Bayes to determine p-values for each gene compared between groups selected by the person performing the query. Literature for the package states that it fits the linear regression on each of the genes, where the rows are the genes and the columns are the samples.

For my analysis of ulcerative colitis and Crohn’s disease samples, I created a dataset where the rows were the samples and the genes were the features. With both normal and disease samples, I was able to create a linear regression model with 1.0 accuracy (baseline = 0.62) for the ulcerative colitis samples and .92 accuracy for the Crohn’s disease (baseline = 0.58) on the small dataset that I had.

However, the gene list I created from the linear regression model was different than the results of the Bioconductor query. For the ulcerative colitis comparison, there were 4 of the same genes and for the Crohn’s disease comparison there were 3 of the same genes for the top 10 differentially expressed genes as to the Bioconductor query.

Top 10 Differently Expressed Genes between Ulcerative Colitis and Normal Samples-Logistic Regression vs Bioconductor

Top 10 Differently Expressed Genes between Crohn’s Disease and Normal Samples-Logistic Regression vs Bioconductor

One of the great things of differential gene expression analysis is the relative ease of confirming results. TaqMan PCR allows researchers to confirm the differential gene expression of a set of genes and look at the relative amount of difference at the same time. The goal of adding different models to microarray analysis is not only to predict the expression but to enhance and add more genes to the answer. Computational biology is a useful tool to distill the variables to a small set, but nothing replaces the verification of expression through PCR.
Logistic Regression
By: Ujjwal Khabrani
Tags: Machine Learning, Regression Algorithm, Regression, Logistic Regression

In this blog, I have discussed one of the most used machine learning algorithms i.e. Logistic Regression. I have briefly discussed its concepts, types, and where it can be used.

Let’s start by understanding what is Regression?

It is a powerful statistical analysis technique. In this a dependent variable of our interest is used to predict the values of other independent variables in a data-set.

As now you have understood about regression we will jump to our main topic

Logistic Regression

Figure 1- Logistic Regression Model

Logistic Regression is a machine learning algorithm that is used for the classification of problems. It is based on concepts of probability and this algorithm is used to solve a classification problem. Let’s understand this with the help of an example:

Ex: When a person applies for a loan from a bank, banks are interested in checking whether the applicant will default in the future or not? Here the logistic regression algorithm comes into play for banks. In this logistic regression is used to make a prediction of the probability, the prediction will fall in [0, 1]. From the outcome, we can predict like if the applicant gets above 0.5 means chances of default are more and the bank should not give the loan, if chances are less than 0.5 then the bank should give the loan.

Figure 2- Graph of Logistic Regression

The range of linear regression is from negative infinity to positive infinity, not in [0, 1]. Then the sigmoid function is introduced to solve this problem. The sigmoid function gives an S-shaped curve and saturates when its argument is very positive or very negative.

Figure 3- Sigmoid Function

Types of Logistic Regression:

1. Binary Logistic Regression: The target variable has only two possible outcomes such as Spam or Not Spam, Cancer, or No Cancer.

2. Multinomial Logistic Regression: The target variable has three or more nominal categories such as predicting the type of Wine.

3. Ordinal Logistic Regression: the target variable has three or more ordinal categories such as restaurant or product rating from 1 to 5.

Use Cases

Following are the use cases where we can use logistic regression:

Weather Prediction, Determining Illness, Demo, Collecting Data, Analyzing Data, Data Wrangling, Test and Train Data

Figure 4- Weather prediction through Logistic regression model

Conclusion:

In this blog, I have presented you with the basics of logistic regression and its uses. I hope it was helpful to you. Happy Reading!!
COUSERA Machine Learning Week07
By: Atsushi Ito
Tags: Kernel, Logistic Regression, Support Vector Machine, Neural Networks, Machine Learning

atsss/cousera_ml

You can't perform that action at this time. You signed in with another tab or window. You signed out in another tab or…
Super easy Python stock price forecasting(using Logistic regression) Machine learning
Tags: Logistic Regression, Python, Stock Price Forecast, Machine Learning

Super easy Python stock price forecasting(using Logistic regression) Machine learning

Machine learning for forecasting up and down stock prices the next day using logistic regression in Python 10mohi6 ·Dec 3, 2020

1. tool installation

$ pip install scikit-learn pandas_datareader

2. file creation

3. execution

$ python pred.py

That’s super easy!

4. result

As a result of calculation with the same data and features, DNN, LogisticRegression, BernoulliNB are the best among XGBoost, DNN, LSTM, GRU, RNN, LogisticRegression, k-nearest neighbor, RandomForest, BernoulliNB, SVM.

XGBoost 0.5119047619047619

DNN 0.5496031746031746

LSTM 0.5178571428571429

GRU 0.5138888888888888

RNN 0.5376984126984127

LogisticRegression 0.5496031746031746

k-nearest neighbor 0.5198412698412699

RandomForest 0.49603174603174605

BernoulliNB 0.5496031746031746

SVM 0.5396825396825397

5. reference
Logistic Regression — Explained and Implemented
By: Manmohan Dogra
Tags: Google Colab, Logistic Regression, Binary Classification, Classification Algorithms, Machine Learning

1. Brief Explanation of Logistic Regression

Despite having the word ‘regression’ in its name, Logistic Regression is a kind of binary classification algorithm. It is named ‘Logistic Regression’ because it’s similar to Linear Regression. The term “Logistic” is taken from the Logit function that is used in this method of classification.

It is a Binary-Classification technique, therefore the output variable is dichotomous in nature. i.e, always in contrast with each other. Either the output would be (0 or 1) or (yes or no).

This technique has applications such as spam filtering, checking the presence of a disease, etc

Logistic regression performs pretty well only with the dependent variables. i.e, say

[x1, x2, x3 ……xn] are the features of our data, and

[y] is the target label of the data, then

our features [ x1, x2, x3 …… xn ] depending more on the target label [y] will perform much better than the features[ x1, x2, x3 …… xn ] not depending on the target label [y].

In layman terms: To predict the presence of diabetes using the depending features such as body temperature, sugar level, age of the patient would give more accurate results, compared to predicting the presence of diabetes using the patient's age, location, and gender.

Traditionally, this logistic regression is being used for binary classification. But, it can also be used for multi-class classification.

Multinomial classification: It is used for multiclass/categorical classification which is a non-traditional/special linear regression case. This method uses log of odds as the dependent variable.

LR uses the logit function to predict the probability of occurrence of a binary event.

Uses sigmoid function(0,1)

Sigmoid function

where (y) has a value of:

Value of y

Linear vs. Logistic

Linear Regression Vs Logistic Regression

Linear regression is a regression technique and hence gives continuous output i.e, the intermediate target values are not binary, therefore would a better choice for applications such as House pricing, stock price prediction, etc.

Logistic regression being a classification technique will give discreet output such as 0 or 1, to classify the input to one of the target labels. therefore LR is a better option for applications such as spam mail detection or cancer detection.

Advantages

- Low computation, widely used

Disadvantages

- Not apt for multi-class, can’t solve non-linear problems.

- It will not perform well with independent variables.

2. Implementation

Implementing Logistic regression for diabetes detection in a person using features such as age, body mass, skinfold thickness, blood pressure, etc., and predicting if the person has diabetes(1) or not(0).

Conclusion

Logistic Regression is one of the most used binary classification techniques. Through this article, we briefly learned about logistic regression, its applications, advantages. Finally, we implementing LR using the ‘scikit-learn’ for detecting if a person has diabetes or not.
14 — Logistic Regression. In statistics, the logistic model (or…
By: Meeraj Kanaparthi
Tags: Machine Learning, Data Science, Logistic Regression

In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one.

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression[1] (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled “0” and “1”. In the logistic model, the log-odds (the logarithm of the odds) for the value labeled “1” is a linear combination of one or more independent variables (“predictors”); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled “1” can vary between 0 (certainly the value “0”) and 1 (certainly the value “1”), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable this generalizes the odds ratio.
Understanding Logistic Regression (Part 1)
By: Naresh Kumar
Tags: Deep Learning, Logistic Regression

Linear and Logistic regression concepts are considered to be the best way to start learning ‘Deep Learning’, and that is very true, but what many people do not think about is the mathematics behind these concepts that really matters. Calculus is your best friend here. In this series of stories, I shall try and explain the concepts of Logistic Regression without using Sci-Kit learn. Keep in mind that ‘Coding is the best way to learn to code’.

Why Logistic Regression?

Logistic regression introduces you to the concept of probability in the form of ‘Maximum Likelihood’ problem and in future we will understand, how gathering a bunch of logistic regression layers, we can create a neural network. (Yes it is that easy)

All this is exciting, but the fundamental purpose of using Logistic Regression is for classification. (specifically binary classification). Later in this series, we will see that logistic regression can actually perform multi-class classification.

Let us start with understanding the problem of classification first. Let us create two data clouds using simple python code and try to visualize things.

import numpy as np

import matplotlib.pyplot as plt

N = 400 #Number of data points

D = 2 #Number of classes

X=np.random.randn(N,D)

T = np.array([0]*(N//2)+[1]*(N//2))

X[0:(N//2),:] = X[0:(N//2),:]-2*np.ones(((N//2),D))

X[(N//2):,:] = X[(N//2):,:]+2*np.ones(((N//2),D))

plt.scatter(X[:,0],X[:,1],c=T)

The output to this code is shown in the left figure below and we want to find a line that can classify the points properly as shown in the right image.
Best and Worst Cases of Machine-Learning Models — Part-1
By: Chandrasekhar Gundluru
Tags: Logistic Regression, Machine Learning, Knn Algorithm, Machinelearning Models, Svm

Introduction:

It's very important to know where our model works well and where it fails. If there is a low latency requirement, definitely KNN will be a worse choice. Similarly, if data is non-linear, then choosing logistic regression is not good so let's dive deep into the discussion and find the pros and cons of models.

1) KNN:

taken from https://www.fromthegenesis.com/pros-and-cons-of-k-nearest-neighbors/

Best cases:

KNN will outperform a logistic regression and linear SVM when the data is not linear.

When there is no latency requirement, KNN performs well on data with few dimensions.

Training is not required for the KNN, so new data can easily be handled by the model.

Limitations:

large datasets: As KNN is a distance-based model, the cost of calculating the distance from query point to every point in data is very high. So when the data is large Knn is not preferred.

As KNN is a distance-based model, the cost of calculating the distance from query point to every point in data is very high. So when the data is large Knn is not preferred. Different Scaled Features: Standardization or normalization is a must before distance measure.

Standardization or normalization is a must before distance measure. Imbalanced data: when data is imbalanced, then KNN predicted values are biased towards the majority class.

when data is imbalanced, then KNN predicted values are biased towards the majority class. KNN doesn’t manage well on large dimensional data because of the curse of dimensionality effect.

data because of the curse of dimensionality effect. Random data: If data is heavily randomized, then the Performance of KNN will worse.

2) Logistic Regression:
Visualisasi data dan Regresi Logistik data Iris
By: Muhamad Agus
Tags: First Try, Data Visualization, Logistic Regression

We are better than we think, keep trying and believe we can grow more. Until realize that we can keep growing
In this blog we are going to discuss how to create a Machine Learning model that will recognize…
By: Flansha Jain
Tags: Logistic Regression, Handwriting Recognition, Matplotlib, Scikit Learn, Machine Learning

Recognizing Handwritten Digits using Logistic Regression

In this blog we are going to discuss how to create a Machine Learning model that will recognize handwritten digits.

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently. Included among the other applications that may come to mind is OCR (Optical Character Recognition) software. OCR software must read handwritten text, or pages of printed books, for general electronic documents in which each character is well defined. But the problem of handwriting recognition goes farther back in time, more precisely to the early 20th Century (1920s), when Emanuel Goldberg (1881–1970) began his studies regarding this issue and suggested that a statistical approach would be an optimal choice. To address this issue in Python, the scikit-learn library provides a good example to better understand this technique, the issues involved, and the possibility of making predictions.

Here, I am using Jupyter Notebook to create this model. In this we are going to use two basic python libraries namely scikit-learn and matplotlib.

Import the Python Libraries and from scikit-learn library import the set of digits (we will use this data set to train our model).

Figure 1

2. Plotted data of digits module using matplotlib.pyplot and using target function find its output

Figure 2

3. Now, we have our dataset ready. We have split this dataset into two parts i.e. training data and testing data by using train_test_split() method available in sklearn.model_selection. Then we have made a Logistic Regression model using sklearn.linear_model and trained this model by using fit() method.

Figure 3

4. Checked the accuracy of the model using score() method. Our model is 95.55% accurate.

Figure 4

5. Then we plotted the digits at positions 90–93 and predicted their values and the results came out to be accurate.

Figure 5

Hence we recognized handwritten digits using Logistic regression.

I am thankful to mentors at https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. Thank you www.suvenconsultants.com.
Speed is a feature: Introducing Fwumious Wabbit
By: Andraz Tori
Tags: Factorization Machine, Logistic Regression, Automl, Data Science, Machine Learning

We are announcing open source release of Fwumious Wabbit, an extremely fast implementation of Logistic Regression and Field-Aware Factorization Machines written in Rust.

At Outbrain we are invested in fast on-line machine learning at a massive scale. We don’t update our models once a day or even once an hour, we update them every few minutes.

This means we need very fast and efficient model research, training and serving. We’ve evaluated many options and concluded that Vowpal Wabbit is the most efficient tool out there in terms of cpu cycles. To the best of our knowledge this used to be the fastest online learning you could get.

Today we are introducing Fwumious Wabbit — a machine learning tool for logistic regression and FFM that is partly compatible with Vowpal Wabbit. Fwumious Wabbit’s main feature is simply speed.

Here’s the comparison to Vowpal Wabbit on a synthetic dataset. Also in our tests Tensorflow on the CPU was an order of magnitude slower than Fwumious.
Logistic Regression Overview and its Error Metrics
By: Nandini Sekar
Tags: Logistic Regression, Roc Curve, Classification Algorithms, Classification Report, Confusion Matrix

Logistic in Short

Overview:

Logistic Regression is the extension of ‘Linear regression’. Target variable in linear regression model will be continuous. Whereas in this algorithm the target variable will be discrete. So if our prediction needs to be discrete, we can use Logistic regression. We need not get carried away from the name as it is holding “regression”. This algorithm is built for classification problems.

Classification problems are also called as binary problems, where the output will be between 2 classes. It is either 0 or 1/Yes or No.

If there are greater than 2 classes to classify it is called as Multi class classification

Classification Problems can be as follows:

1. Email is Spam / Not a Spam

2. Passed / Failed

3. Survived / Not Survived

4. Loans — Approved / Rejected

Function used in this algorithm is “Sigmoid” or “Logistic” which is represented as

Sigmoid Curve

We have a concept called Decision Boundary(db) in Logistic regression. This is a line which splits from one class to other class. By default db will be 0.5 , the process goes like if the resultant data are less than (db)0.5 it can be classified as 0 & if data are greater than (db)0.5 it is 1 and vice versa.

The fundamental application of logistic regression is to determine a decision boundary for a binary classification problem. We need to adjust the decision boundary levels after usual predictions to check on accuracy levels and see how it varies. That is we need to vary the DB value from 0.5(default) to any value between 0 to 1.

Error Metrics:

These are some frequently used metrics in industry for classification problems to measure accuracy percentages and error levels they are as follows:

a. Confusion Matrix, b. Classification Report, c. ROC Curve & d. Accuracy Score

I) Confusion Matrix below is used to find the amount of values which are predicted correctly & wrongly.

Confusion Matrix

Here FP is called as Type I error and FN are called as Type II error. Where both the Type I and II errors are the values which are predicted wrongly. Similarly TP and TN are the values which are predicted correctly

II) Classification Report: This includes 3 parameters which are -

a. Precision: This is defined as Number of positive patterns predicted correctly, by total number of patterns in positive class.

b. Recall: This is defined as fraction of the patterns that are correctly classified

c. F1 Score: This is defined as Harmonic mean between Precision and Recall values

III) Accuracy Score: This is the usual metric which predicts the overall accuracy of the model.

IV) ROC Curve: “Receiver Operating Characteristic Curve” is the score which lies between 0 to 1. 0 stands for Bad and 1 stands for Good. 0.5 is better. If ROC score is 0.78 then it means 78% of predicted values are correct and rest 22% are predicted wrongly.
Recognizing Handwritten Digits with Scikit-Learn
By: Eshaan Patil
Tags: Logistic Regression, Scikit Learn, Matplotlib

This article presents recognizing the handwritten digits (0 to 9) using the famous digits data set from Scikit-Learn, using a classifier called Logistic Regression. Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents.

First of all import all the needed libraries:

Import the dataset:

Following is an example of a digit in our dataset. It consists of 64 pixels (8X8).

The targets are contained in the digit. targets array

The 1792nd element in our data-set

Splitting our Dataset into training and testing sets

Now let’s split our Dataset into training and test sets to make sure that after we train our model, it can generalize well to new data.

Importing the model we want to use.

Here we will be using Logistic Regression. Logistic regression is a linear classifier and therefore used when there is some sort of linear relationship between the data.

Making an instance of the Model

Training the Model

Here the Model is learning the relationship between digits (x_train) and labels (y_train)

Predicting the labels of new data

Using the information the Model learned during the training process.

Measuring the performance of our Model

To test the accuracy of our predictions we can use accuracy_score

From this article, we can see how easy it is to import a dataset, build a model using Scikit-Learn, train the model, make predictions with it, and finding the accuracy of our prediction(which in our case is 96.67%).
“Logistic Regression” Nedir?
By: Umut Çaylı
Tags: Logistic Regression, Data Science, Deep Learning, Python, Machine Learning

Logistic Regression for Machine Learning - Machine Learning Mastery

Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to…
What is causing Increasing Anxiety Levels in this Pandemic time?
Tags: Covid 19, Logistic Regression, Supervised Learning, Analytics, Machine Learning

Objective: As the world is witnessing an unprecedented outbreak of mass proportions, and all of us are doing our bit by ensuring ‘social distancing’, essentially locking ourselves inside our own homes, totally avoiding social interactions. We wanted to understand what are the different attitudes/actions which are causing increased anxiety levels in this pandemic.

Data source: we have considered India data from the Kalos 2020 a survey done at an international level across 15 nations around 7,000 respondents by Toluna & Kaleidosense.

Approach: As in the Kalos 2020 respondents were asked to rate 4 point scale on various attitudes to life change due to covid , such as Quality time, Learning new things, Retail Experience, WFH , outdoor activities..etc. and also captured the individuals anxiety levels . In order to find what is driving increased anxiety levels we categorized the increasing anxiety levels into positive and negative and find the drivers what is causing positive in increased anxiety levels. This analysis we have done at overall level as well as among males and females to check is there any differences emerge.

As being Increased Anxiety levels is being dichotomous (positive/negative) variable, we have applied a popular well known classification technique call logistic regression and regressed this increased anxiety levels on various Quality, Learning new things , Retail Experience, WFH, Outdoor activities related attributes and selected the statistically significant attributes which are driving the increased anxiety levels with the accuracy of 90%.

Then we have normalized the each attitude importance from the above table and then calculated relative importance through which we can order these attributes as per their order importance on causing increased levels of anxiety. And the below is the same for at an overall level as well as among males and females.

Impact: From the above. At an overall level we can see that somehow Quality family time is over rated than actual causing more anxiety followed by not able to enjoy the earlier retail experience and not able to enjoy the routine life ,due to not able spend more time outdoor and scary of Coughing/Sneezing in public and also noticed there is clear difference for increasing anxiety among males and females