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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
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
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
Model Deployment in Android Model Deployment in Iphone

7 Popular Feature Selection Routines in Machine Learning

pavan
pavan Last Updated : 16 Mar, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Best ways for Feature Selection

The general principle in Data Science is more quality training data results in a better Machine Learning model. It is valid for the number of situations but not for the number of features. A real-world dataset consists of unnecessary features that may impact the performance of the model.

Best ways for Feature Selection

One needs to be selective in terms of features chosen for the modeling. The dataset contains a lot of independent variables where only a few are useful and others not. Selecting the best set of features from the combinations results in a polynomial approach that requires a polynomial time complexity. We will discuss seven ways to choose the best features for training a robust ML model in this post.

Domain Knowledge

A Data Scientist or ML engineer with domain knowledge about the problem would help choose the best features and set of variables for any case study. People with intuition about the data or domain knowledge helps in feature engineering and selection of best features. For example, consider the car price prediction problem, where features such as the manufacture year, model, fancy license number are unique factors for deciding the car price.

Missing Values

The real-world datasets usually consist of missing values, occurred due to failure to record or data exploitation. We have various techniques for imputation of missing values, but that does not match the real data. Hence, a model trained on features that consist of missing values may not yield better performance.

It is better to drop the columns or variables that contain missing values more than a certain threshold. We can drop the ‘cabin’ feature that has a lot of missing values. One can refer to the below image generated from titanic data.

Missing Values feature selection

Source: Author

 

Correlation with the Target label

Correlation between the target class label and features determines how correlated each variable with references to the target class. There are many correlation techniques such as Pearson, Spearman, Kendall, etc. used for observing correlations between two features.

df.corr() returns with the person correlation coefficient between the features. Variables such as sex, Pclass, fare are highly correlated with the target class label Survived and hence known as key features, from the below correlation heatmap of titanic data. Now features such as PassengerId, SibSp is not correlated with the target variable and may not impact our model performance. Hence these variables can be excluded.

Correlation with the Target label

Source: Author

Correlation between the Features

Correlation between variables results in collinearity, which may affect the model performance. A variable is said to be correlated with others if they have a high correlation. Hence, a change in one feature results in a variation of other correlated variables.

From the above correlation heatmap of Titanic data, the Pearson correlation coefficient between Pclass and Fare is high, so the change in one variable will negatively impact the other variable.

 

Principle Component Analysis (PCA)

PCA is a dimensionality reduction method used to extract features from the dataset. It reduces the dimensionality of the dataset to a lower dimension by using matrix factorization. It projects the data completely into a lower dimension preserving the variance.

We use this method to reduce the features when the dimensionality of the data is very high, and removing the redundant variables is a tedious task. This method reduces the dataset with various variables into a dataset with the desired number of features.

The below PCA plot is trained on the dataset from Ionosphere Dataset from the UCI machine learning repository

Source: Author

 

The total number of dimensions: from the above plot, 34 Observations.
90% of the variance is preserved for 15 dimensions.
80% of the variance is preserved for 9 dimensions.

Reducing the dimensions to fifteen preserves 90% of the variance and selects the most useful features.

 

Forward Feature Selection

Forward Feature Selection

Source: Author

 

 

Forward or Backward feature selection helps to find a subset of the best performing features for the ML model. If there are n features for a given dataset, variables are selected based on the previous result inference.

The forward feature selection techniques follow:

  1. Evaluate the model performance after training by using each of the n features.
  2. Finalize the variable or set of features with better results for the model.
  3. Repeat the first two steps until you obtain the desired number of features.

Forward Feature Selection is a wrapper method to choose the best subset of features. The backward Feature Selection technique is just the contrast of forwarding Feature selection, where initially all the variables are chosen and remove the most redundant features in each step.

 

Feature Importance

Feature Importance is the list of features that the model considers being important. It gives an importance score for each variable, describing the importance of that feature for the prediction. Feature Importance is an inbuilt function in the Sk-Learn implementation of many ML models.

Feature importance scores help to identify the best subset of features and training a robust model by using them.

 

Conclusion

Feature selection is a valuable process in the model development pipeline, as it removes unnecessary features that may impact the model performance. In this post, we looked at seven techniques to choose the best set of features from data. One can use these hacks in your data science model for better performance.

Apart from the above mentioned, there are various other hacks to remove the redundant features, like remove variables having a low variance, chi-square test.

I hope you have found this article useful and have a great day, Thank you.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

pavan
pavan

Embarking on a transformative odyssey through the realms of AI, ML, and NLP, I've woven a tapestry of experience over three dynamic years. Amidst the digital symphony, I now find myself enraptured by the artistry of Generative AI, sculpting the future of innovation. As I dance with colossal language models, each keystroke becomes a brushstroke, painting the canvas of possibility in this ever-evolving technological landscape.

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