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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
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Most Common Feature Selection Filter Based Techniques used in Machine Learning in Python

S
SAGNIK Last Updated : 05 Oct, 2020
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

As a programmer who is engaged in the field of AI and Machine Learning related activities, it is very important for him/her to perform AI-related stuff most efficiently. The efficient way here in this context means that he/she should be well capable of getting the best predictive analysis result when feeding it to the Machine learning model and to achieve this efficiency many preprocessing steps are required before the prediction to be made.

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These preprocessing steps are data handling, manipulations, creation of features, updating the features, normalizing data, etc. From all these preprocessing steps present out there one of the main steps is to do the feature selection. As we know that Machine learning is an iterative process in which the machine tries to learn based on the historical data we are feeding to it and then makes predictions based on the same.

The data we feed is nothing but a combination of rows and columns of information bound within. This columnar information is independently called a feature. Here, the categorization of features is made into two parts that are, a dependent feature and an independent feature. Dependent features are those which we need to predict that is, our target feature, and the independent ones are those on which we make the Machine learning model.

It is very important that the independent features to be highly related to the dependent one. But, this fails to happen in the real world and we may end up with a poor model. To help in improving the efficiency we need to perform feature selection. Generally, there a few feature selection techniques that one should definitely know to help improve their ML model and the same has been discussed below in detail:

 

Common Feature Selection Filter Based Techniques

1. Feature Selection with the help of Correlation:

This is the most common type of feature selection technique that one should know to get a fairly good model. The feature selected with the help of this technique is based on the statistical relationship that we were taught in our school times. This means that features that have a high correlation with the target variable are taken into consideration and the ones with the low correlation with the target are neglected.

This can also be done with the help of only independent features i.e., if the correlation between two independent features is high then we may drop one of them and if the correlation is low then we can proceed further. The statistical equation of the correlation (X and y) is given as:

Covariance(X,y) / Standard deviation (X) . Standard deviation (y)

The correlation method we generally prefer is the Pearson Correlation which has a range of -1 to +1. Here — means strong negative correlation and +1 means strong positive correlation. The features that have a very low correlation with the target that is, below 0.3 or 30% are neglected.

The correlation can be performed with the help of Python very easily and the same has been depicted below with the help of Seaborn and Pandas library:

Feature Selection Filter
Feature Selection Filter
Feature Selection Filter

 

2. Feature Selection with the help of the Chi-Square Test:

This is a form of non-parametric test (a test wherein median is an important parameter) in which feature selection is done with the help of hypothesis testing and p-value. The feature selection is only suited to categorical features or features having discrete data contained within it. The continuous features are not taken into account and therefore should not be used while performing this test. The formula for this type of test is given as:

Summation (Observed Value – Expected Value) / Expected Value

If the p-value is less than 0.05 then we reject the null hypothesis and go with the alternate hypothesis in this type of test. The implementation of Chi-Square with the help of the Scikit Learn library in Python is given below:

 

3. Feature Selection with the help of Anova Test:

A feature selection technique is most suited to filter features wherein categorical and continuous data is involved. It is a type of parametric test which means it assumes a normal distribution of data forming a bell shape curve. There are many types of Anova test out there and a user can try out these as per their need. The assumption here is made as per the hypothesis testing where the null hypothesis states that there is no difference in the means and alternate says that there is a difference. If the p-value is less than 0.05 then the null hypothesis is rejected and the features are selected or else dropped if the null hypothesis becomes true. The implementation of this feature selection technique is given below with the help of Scikit Learn library:

 

Conclusion

So, these are the most common feature selection filter based techniques that one should learn to make their model a good fit for the Machine learning algorithm and to attain higher accuracies.

S
SAGNIK

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