Machine Learning Tutorial Chap 7 | Part-5 Logistic Regression | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 05/07/2019

Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist and an Instructor at GreyAtom will take us through Logistic Regression in machine learning through this introduction series. 

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (i.e a variable that has only two values). In logistic regression, the dependent variable is binary or dichotomous, i.e. it only contains data coded as 1 (TRUE, success, pregnant, etc.) or 0 (FALSE, failure, non-pregnant, etc.). The goal of logistic regression is to find the best fitting (yet biologically reasonable) model to describe the relationship between the dichotomous characteristic of interest (dependent variable = response or outcome variable) and a set of independent (predictor or explanatory) variables. Logistic regression generates the coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest.Rather than choosing parameters that minimize the sum of squared errors (like in ordinary regression), estimation in logistic regression chooses parameters that maximize the likelihood of observing the sample values.


This is the 5th in 6 videos about Logistic Regression in Machine Learning. 

In this video, we will explore the Accuracy, Precision, Recall and F1 score. 

Accuracy - Accuracy is the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations. One may think that, if we have high accuracy then our model is best. Yes, accuracy is a great measure but only when you have symmetric datasets where values of false positive and false negatives are almost same. Therefore, you have to look at other parameters to evaluate the performance of your model.

Precision - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

Recall (Sensitivity) - Recall is the ratio of correctly predicted positive observations to the all observations in the positive class.

F1 score - F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall


Complete Playlist for the Course: https://bit.ly/2Q1zvK6 

After completing our 6-part Series on Logistic Regression in Machine Learning, you will be able to do the following:

Understand and apply the Logistic Regression algorithm
Understand Gradient Descent in Logistic Regression 
Get in-depth knowledge about the various Evaluation Metrics 
Learn how to tune hyperparameters using Grid Search and Random Search


Here’s the full syllabus of our 6-part video on Logistic Regression in Machine Learning:


Why Logistic over Linear Regression ? 
Decision Boundary 
Hyperparameter Tuning 
Cost Function 
Gradient Descent  
Evaluating Matrix 
Precision and Recall 
Logarithmic Loss 

#machinelearningtutorial #LogisticRegression #DataScience101 #Greyatom

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