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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 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

Discovering the shades of Feature Selection Methods

S
Swetha Last Updated : 23 Apr, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Introduction

The data consists of a two-dimensional array of categorical or continuous data points for each field also called features that have a great impact on the dependent variables that need to be predicted using any Machine learning models. Mindless about the different features present in the dataset, how we make the best out of the data is what affects the performance of the forecasting model on a large basis.

So how do we exhibit our data to leverage it for a specific application? ……. Feature Engineering

Feature Engineering

Feature engineering is one of the important tasks that need to be performed in designing any data science-related problem statement because exhibiting the data in a proper way can have an enormous influence on the evaluation of the model. In other words, this method involves experimenting with all combinations of features to highlight the key ones thereby isolating those that are irrelevant. It requires domain knowledge for selecting the most appropriate features that will bring out an accurate result.

Feature Selection

Feature selection or variable selection is a cardinal process in the feature engineering technique which is used to reduce the number of dependent variables. This is achieved by picking out only those that have a paramount effect on the target attribute. By employing this method, the exhaustive dataset can be reduced in size by pruning away the redundant features that reduce the model’s accuracy. Doing this will help curtail the computational expense of modeling and in some cases may also boost the accuracy of the implemented model.
For example let us consider, Heart Failure Clinical dataset containing medical records of patients who had left ventricular systolic dysfunction and with a history of heart failure.
feature selection methods head

So here we have 12 features enlisted to carry out the forecasting process. Clearly using our expertise in this field we can tell that whether a person suffers from heart failure or not can be predicted using the Gender of the person. Nowadays a person irrespective of age and gender is suffering from CVD, so it may not be a key feature for prediction and hence can be isolated from training the model.
Increasing the features can make the model complex to learn and can also cause overfitting. In order to generalize the feature selection methods/models, we will look into a few feature selection techniques to reduce the dimensionality of the dataset. There are 3 basic approaches: Model-based approach(Extra-tree classifier), Iterative search (Forward stepwise selection), and Univariant statistics (Correlation and Chi-square test).
The feature selection methods we are going to discuss encompasses the following:

 

  • Extra Tree Classifier
  • Pearson correlation
  • Forward selection
  • Chi-square
  • Logit (Logistic Regression model)

 

Extra Tree Classifier

This is a Model-based approach for selecting the features using the tree-based supervised models to make decisions on the importance of the features. The Extra Tree Classifier or the Extremely Random Tree Classifier is an ensemble algorithm that seeds multiple tree models constructed randomly from the training dataset and sorts out the features that have been most voted for. It fits each decision tree on the whole dataset rather than a bootstrap replica and picks out a split point at random to split the nodes.
The splitting of nodes occurring at every level of the constituent decision trees is based on the measure of randomness or entropy in the sub-nodes. The nodes are split on all variables available in the dataset and the split that results in the most homogenous sub-child is selected in the constituent tree models. This lowers the variance and makes the model less prone to overfitting.
X=heart.iloc[:,0:12]
Y=heart['DEATH_EVENT']
from sklearn.ensemble import ExtraTreesClassifier
model = ExtraTreesClassifier()
model.fit(X, Y)
print(model.feature_importances_)
feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(13).plot.bar()
plt.show()
list1=feat_importances.keys().to_list()

Pearson Correlation

Pearson Correlation is used to construct a correlation matrix that measures the linear association between two features and gives a value between -1 and 1 indicating how related the two features are to one another. This measures the degree to which two features are interdependent by computing the association between each feature and the target variable, the one exerting high impact on the target can be picked out.
Pearson Correlation feature selection method

 

where x is the ith value of the variable x, xis the average value of sample attribute x, n is the number of records in the dataset and, x and y are the independent and target variables. A value of 1 indicates a positive correlation, -1 indicates a negative correlation and 0 indicates no correlation between the features.
#Correlation with the output variable
cor_target = abs(corr["DEATH_EVENT"])
#Selecting highly correlated features
relevant_features = cor_target[cor_target>0.2]
list4=relevant_features.keys().to_list()
list4

 

Chi-squared

A chi-square test is used in statistical models to check the independence of attributes. The model measures the degree of deviation between the expected and actual response. The lower the value of Chi-square, the less dependent the variables are to one another, and the higher the value more is their correlation. Initially, the attributes are assumed to be independent which forms the null hypothesis. The value of the expected outcome is computed using the following formula:
Feature selection methods chi-square
Chi-square is obtained from the expression that goes as follows:
Where, i is in the range (1,n), n is the number of dataset records, Oi is the actual outcome, and Eiis the expected outcome.
chi2_features = SelectKBest(chi2, k = 6) 
X_kbest_features = chi2_features.fit_transform(X, Y)
mask=chi2_features.get_support()
new_feature=[]
for bool,feature in zip(mask,X.columns):
  if bool:
    new_feature.append(feature)
list3=new_feature
list3

Forward Selection

Forward selection is a wrapper model that evaluates the predictive power of the features jointly and returns a set of features that performs the best. It selects the predictors one by one and chooses that combination of features that makes the model perform the best based on the cumulative residual sum of squares. This process continues till there is no impact on the performance of the model with the incoming variable.
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import roc_auc_score
from mlxtend.feature_selection import SequentialFeatureSelector
forward_feature_selector = SequentialFeatureSelector(RandomForestClassifier(n_jobs=-1),
           k_features=6,
           forward=True,
           verbose=2,
           scoring='roc_auc',
           cv=4)
fselector = forward_feature_selector.fit(X,Y)

Logit Model

This is the Logistic regression-based model which selects the features based on the p-value score of the feature. The features with p-value less than 0.05 are considered to be the more relevant feature.
import statsmodels.api as sm
logit_model=sm.Logit(Y,X)
result=logit_model.fit()
print(result.summary2())

logit

 

Now we have applied different feature selection models to our dataset, we will see the features selected by each module,
different methods

Now that the features have been selected, we are good to apply any supervised classification models to predict the outcome.
You can find the full code of this project here.
Happy learning !!

You can reach out to me through LinkedIn

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

S
Swetha

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