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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

Get to Know All About Evaluation Metrics

sanjay
sanjay Last Updated : 30 Sep, 2022
6 min read

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

Introduction

Evaluation metrics are used to measure the quality of the model. Selecting an appropriate evaluation metric is important because it can impact your selection of a model or decide whether to put your model into production. 

Evaluation Metrics

The mportance of cross-validation: Are evaluation metrics enough? Read more here.

Evaluation Metrics for Classification

To understand Classification evaluation metrics, let’s first understand the confusion metric,

Confusion Metric:

  • It is a tabular data format used to visualize classification problems’ performance.
  • It is also known as Error Matrix and is used for Supervised Classification algorithms, typically for Binary-Classification algorithms, though it can be used for any classification algorithm.
  • It compares how your prediction has fared in comparison to actual values.

We will take the Youtube Kids example, where you are trying to classify whether a video is Kids safe or not. Whether it is fine to watch a particular video or not.

Confusion Matrix has 4 values,

True Positive (TP):

  • The model has predicted a positive, and the actual value is also positive.
  • A video is predicted as Kidsafe, and actually, it’s Kidsafe.

True Negative (TN):

  • The model has predicted a negative, and the actual value is also negative.
  • A Video is predicted as not Kidsafe, and in reality, it’s not Kidsafe.

False Positive (FP):

  • The model has predicted a positive, but the actual value is negative.
  • They are also known as Type I error.
  • A video is predicted to be Kidsafe, but it’s not Kidsafe. It may be violent, or it has adult content which is not good for Kids.

False Negative (FN):

  • The model has predicted a negative, but the actual value is positive.
  • They are also known as Type II errors.
  • A video is predicted as not Kidsafe; in reality, it’s Kidsafe. It may be a cartoon, but still, it is marked as violent.

Now let’s try to understand different evaluation metrics,

1. Accuracy:

  • Accuracy represents the number of correctly classified data instances over the total number of data instances.
  • If data is not balanced, it will not be a good evaluation metric, as Accuracy will be biased for classes with a higher number of counts. We can opt for Precision or Recall

Accuracy = (TP + TN) / (TP + FP + FN + TN)

2. Precision:

  • How many of the correctly predicted cases turned out to be positive?
  • It is useful when a False Positive is a major concern than a False Negative.
  • Precision should be as high as possible.

Precision = TP / (TP + FP)

3. Recall:

  • Also known as Sensitivity or True Positive Rate
  • How many of the actual positive cases were able to predict correctly with our model
  • It is more useful when a False Negative is a major concern than a False Positive.
  • Recall should be as high as possible.

Recall= TP / (TP + FN)

4. F1 Score:

  • It is the harmonic mean of Precision and Recall.
  • F1 Score balances both Precision & Recall; if any one is small, the F1 Score will also be smaller. That’s what makes it different than Accuracy.

F1 Score = ( 2 * Precision * Recall) / (Precision + Recall)

5. Specificity:

  • How many of the actual negative cases could predict correctly with our model?

Specificity = TN / (TN + FP)

6. AUC-ROC Score:

  • It stands for Area Under the Curve – Receiver Operating Characteristics
  • ROC graph is used to evaluate the classification model performance for the different threshold values.
  • AUC is the measure of the ability of a classifier to distinguish between classes.
    • If AUC = 1, then the classifier perfectly differentiates the classes.
    • If 0.5<AUC<1, then the classifier has a high chance of differentiating between classes.
    • If AUC=0.5, then the classifier is not differentiating Positive and Negative classes well.

Precision-Recall Tradeoff: It is expected to have both Precision and Recall high percentages. But if you go for Higher Precision, your Recall lowers, and vice versa. Ex. For Youtube Kidsafe, False Positive is a concern, we don’t want kids to see Violent videos, but it may cause missing some Kidsafe videos like cartoons.

Evaluation Metrics for Regression

1. Mean Absolute Error(MAE):

  • Mean Absolute Error is the sum of the predicted values minus the true values divided by the number of predicted values.

 

  • This evaluation metric is very helpful if you expect outliers in your dataset.

2. Mean Squared Error(MSE):

  • Mean Squared Error is the sum of the square of the difference between predicted values & the true values divided by the number of predicted values.

3. Root Mean Squared Error(RMSE):

  • Root Mean Squared Error is the root of the sum of the square of the difference between predicted values & the true values divided by the number of predicted values.

 

  • This evaluation metric is beneficial if you expect outliers in your dataset.
  • If the dataset has more outliers, RMSE becomes larger than MAE.

4. R squared:

  • R-Squared is a statistical measure presenting the proportion of the variance for a dependent variable that is explained by one or more independent variables in a regression model.
  • R-Squared is the one minus sum of squares of residual divided by the total sum of squares.

 

  • R-Squared is used to find the goodness of the best fit line.
  • The problem with R-Squared is, that it never decreases even if we add a new redundant variable. R-Squared value either stays the same or increases.

5. Adjusted R-Squared:

  • Adjusted R-Squared solves the problem by adding a number of the independent variables (k) in the denominator.
  • The formula of the Adjusted R-Squared is
Evaluation Metrics

where N = number of data points in the dataset

p = number of independent variables.

Evaluation Metrics for Unsupervised — Clustering

1. Rand Index or Rand measure:

  • The Rand Index computes a similarity measure between two clusters by considering all pairs of samples and counting pairs assigned in the same or different clusters in the predicted and actual clusterings.
  • The formula of the Rand Index is:
Evaluation Metrics
  • The RI can range from zero to 1, a perfect match. You can compare Rand Index to Accuracy in Supervised Binary Classification. Check the below calculations,
  • The Rand Index drawback is that it assumes that we can find the ground-truth cluster labels and use them to compare the performance of our model, so it is much less helpful for pure Unsupervised Learning tasks.

2. Adjusted Rand Index:

  • The adjusted Rand index is the corrected-for-chance version of the Rand index.
  • The raw RI score is then “adjusted for chance” into the ARI score using the following scheme:
Evaluation Metrics
  • The adjusted Rand Index is thus ensured to have a value close to 0.0 for random labeling independently of the number of clusters and samples and exactly 1.0 when the clusterings are identical.
  • The adjusted Rand Index can yield negative values if the index is less than expected.

Conclusion

An evaluation Metric is used for assessing the correctness of models. We have multiple evaluation metrics, but selecting the correct evaluation metrics is crucial. The key factor for selection is the data and the type of problem we are trying to solve.

Hopefully, this article helps you to select the correct evaluation metrics for your machine learning problem.

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

sanjay
sanjay

Total 13 years of experience in an Engineering and technology
Strategy Role. Accomplished Data Scientist with a passion for delivering valuable
data through analytical functions and data retrieval methods. Skilled in data analytics, machine learning, natural language
processing, and cloud computing.

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