Feature Selection Techniques in Machine Learning with Python
By: Raheel Shaikh
Tags: Machine Learning, Python, Data Analysis, Data Science, Data Visualization

With the new day comes new strength and new thoughts — Eleanor Roosevelt

We all may have faced this problem of identifying the related features from a set of data and removing the irrelevant or less important features with do not contribute much to our target variable in order to achieve better accuracy for our model.

Feature Selection is one of the core concepts in machine learning which hugely impacts the performance of your model. The data features that you use to train your machine learning models have a huge influence on the performance you can achieve.

Irrelevant or partially relevant features can negatively impact model performance.

Feature selection and Data cleaning should be the first and most important step of your model designing.

In this post, you will discover feature selection techniques that you can use in Machine Learning.

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in.

Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

How to select features and what are Benefits of performing feature selection before modeling your data?

· Reduces Overfitting: Less redundant data means less opportunity to make decisions based on noise.

· Improves Accuracy: Less misleading data means modeling accuracy improves.

· Reduces Training Time: fewer data points reduce algorithm complexity and algorithms train faster.

I want to share my personal experience with this.

I prepared a model by selecting all the features and I got an accuracy of around 65% which is not pretty good for a predictive model and after doing some feature selection and feature engineering without doing any logical changes in my model code my accuracy jumped to 81% which is quite impressive

Now you know why I say feature selection should be the first and most important step of your model design.

Feature Selection Methods:

I will share 3 Feature selection techniques that are easy to use and also gives good results.

1. Univariate Selection

2. Feature Importance

3.Correlation Matrix with Heatmap

Let’s have a look at these techniques one by one with an example

You can download the dataset from here https://www.kaggle.com/iabhishekofficial/mobile-price-classification#train.csv

Description of variables in the above file

battery_power: Total energy a battery can store in one time measured in mAh

blue: Has Bluetooth or not

clock_speed: the speed at which microprocessor executes instructions

dual_sim: Has dual sim support or not

fc: Front Camera megapixels

four_g: Has 4G or not

int_memory: Internal Memory in Gigabytes

m_dep: Mobile Depth in cm

mobile_wt: Weight of mobile phone

n_cores: Number of cores of the processor

pc: Primary Camera megapixels

px_height

Pixel Resolution Height

px_width: Pixel Resolution Width

ram: Random Access Memory in MegaBytes

sc_h: Screen Height of mobile in cm

sc_w: Screen Width of mobile in cm

talk_time: the longest time that a single battery charge will last when you are

three_g: Has 3G or not

touch_screen: Has touch screen or not

wifi: Has wifi or not

price_range: This is the target variable with a value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).

1. Univariate Selection

Statistical tests can be used to select those features that have the strongest relationship with the output variable.

The scikit-learn library provides the SelectKBest class that can be used with a suite of different statistical tests to select a specific number of features.

The example below uses the chi-squared (chi²) statistical test for non-negative features to select 10 of the best features from the Mobile Price Range Prediction Dataset.

import pandas as pd

import numpy as np

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import chi2 data = pd.read_csv("D://Blogs//train.csv")

X = data.iloc[:,0:20] #independent columns

y = data.iloc[:,-1] #target column i.e price range #apply SelectKBest class to extract top 10 best features

bestfeatures = SelectKBest(score_func=chi2, k=10)

fit = bestfeatures.fit(X,y)

dfscores = pd.DataFrame(fit.scores_)

dfcolumns = pd.DataFrame(X.columns)

#concat two dataframes for better visualization

featureScores = pd.concat([dfcolumns,dfscores],axis=1)

featureScores.columns = ['Specs','Score'] #naming the dataframe columns

print(featureScores.nlargest(10,'Score')) #print 10 best features

Top 10 Best Features using SelectKBest class

2. Feature Importance

You can get the feature importance of each feature of your dataset by using the feature importance property of the model.

Feature importance gives you a score for each feature of your data, the higher the score more important or relevant is the feature towards your output variable.

Feature importance is an inbuilt class that comes with Tree Based Classifiers, we will be using Extra Tree Classifier for extracting the top 10 features for the dataset.

import pandas as pd

import numpy as np

data = pd.read_csv("D://Blogs//train.csv")

X = data.iloc[:,0:20] #independent columns

y = data.iloc[:,-1] #target column i.e price range

from sklearn.ensemble import ExtraTreesClassifier

import matplotlib.pyplot as plt

model = ExtraTreesClassifier()

model.fit(X,y)

print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers

#plot graph of feature importances for better visualization

feat_importances = pd.Series(model.feature_importances_, index=X.columns)

feat_importances.nlargest(10).plot(kind='barh')

plt.show()

top 10 most important features in data

3.Correlation Matrix with Heatmap

Correlation states how the features are related to each other or the target variable.

Correlation can be positive (increase in one value of feature increases the value of the target variable) or negative (increase in one value of feature decreases the value of the target variable)

Heatmap makes it easy to identify which features are most related to the target variable, we will plot heatmap of correlated features using the seaborn library.