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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

Interpretation of Performance Measures to Evaluate Models

S
Sitara Last Updated : 30 Mar, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Introduction

In the last year of my bachelor’s degree, I learned an interesting fact during a customs class:

If a person appears suspicious, they detain him and deliberately prolong the interrogation process. He was asked very simple but annoying questions. Here one person asks a question, and another employee stands a short distance away and observes as if he was doing something else. If a person is really carrying a prohibited, harmful, or dangerous item across the border, selling, or importing it, they become overly irritable and overly emotional. Such a person is already considered quite suspicious.

That is, the probability of P (Person = Criminal / Reaction = Emotional) is high. However, this does not mean that all emotional individuals are criminals; If some people are suspicious, it may simply be due to their neurotic or other health problems, current mood, or first trip.

There is a saying, “Where there is no fire, there is no smoke.” That is, if I see smoke, then there is a fire. I wonder how right I am in this opinion?

Let’s say I’m making a fire detector. Thus, this detector should detect fire cases as accurately as possible and not cause a disturbance by giving a false fire signal. That is, the detector should not endanger me or disturb me unnecessarily.

Here, a fire that the detector can give a true warning to is a “True Positive” result. There is a fire (the event is positive) and the detector has identified it correctly (the result is true).

In the absence of fire, the alarm is “False Positive”. There is no fire, but the detector gives a positive signal by mistake. Let me note that this does not mean that the positive is good, but that the affirmation of the incident that took place.

The fact that the detector does not signal in the absence of fire is “True Negative”. No incident occurred and the detector did not signal according to the situation.

Failure to signal in case of fire (this is the most dangerous situation) is “False Negative”. An accident occurs, but the detector can not see it.

We show these 4 cases in the form of the table below.

Evakuate model with confusion matrix
Source
 

We have two types of errors.

Type 1 is believing that something is not wrong, and Type 2 is not believing that something exists (it is not the same as believing that it does not exist).

In Type 1 error we reject the true Null Hypothesis, but in Type 2 error we fail to reject the false Null Hypothesis (we cannot accept Null Hypothesis, we can only fail to reject it due to insufficient data, time interval, or other impediments). 

Generally, Type 1 error is considered more dangerous, because we continue to believe in something that does not exist, and we do not re-investigate. The investigation continues until it finds a Type 2 error. However, this comparison varies from situation to situation. Fire, health problems, accidents, etc. Type 1 error is often more reliable in matters related to human life because even if I am worried by mistake, I will insure myself (call the fire brigade, take vitamins, stay at home, etc.) and the second type of mistake will give me peace of mind and I will be caught unprepared. Otherwise, even if the detector gives the wrong signal, I will search everywhere until I find the problem. As a result, I will not face life-threatening.

To make such a detector, I have to program it (in the language of Data Science, train it), and finally, test it.

Here, our model transforms the prior probability into a posterior with the data we give it. That is before there was historical data of fire cases the probability of fire cases was 50%. Now it has the data to learn. Based on this data, a new result is obtained according to Bayes’ theorem (P (Probability of occurrence of the event | If data is given)). During the test, we construct a matrix similar to the one above. And there we record the number of TP, TN, FP, FN cases. These numbers show how accurate our model is. Such a matrix is ​​called a confusion matrix.

Let’s say our model detects 80% of fires. At first glance, the result is not bad. But when we look at the sentence again, we see that I only find 80% of fire cases. What about when there is no fire? Maybe our detector will activate such an alarm more often in the absence of fire and cause additional inconvenience?

Here, 80% is called the “sensitivity rate”. That is, our model is 80% sensitive to fires and detects 80% of fires. This is only for fires. Taking this as the core value of our model leads to a “base rate fallacy”.

“Base rate” – the percentage of fires that occur (the ratio of the number of fires to the total time, for example, once in 5 years.

We also need to know the “specificity rate”. The degree of specificity is the correct detection of non-fire cases by the detector, ie how accurate the detector can find when there is no fire.

But how can we evaluate our model?

We use the following ratios to evaluate the model correctly.

In the Confusion Matrix, the TP / (TP + FP) ratio is called precision. That is the ratio of the number of times the signal is activated during a fire to the total time the signal is activated (what percentage of alarm cases occur at the right time).

The TP / (TP + FN) ratio is called recall. That is the ratio of the number of fires in which the signal is activated to all fires (what percentage of alarms are activated during a fire). Recall and sensitivity are the same concepts.

Besides, there is an accuracy rate. It is also equal to the ratio (TP + TN) / (TP + FP + TN + FN). That is the ratio of the times when all the alarms work properly (activated in case of fire, not activated in case of fire) to all cases (note that for highly imbalanced data we rely on F1 score rather than accuracy rate).

These 3 ratios are very important to evaluate the model. It should also be noted that, in fact, relative values ​​are more important than absolute values ​​for any assessment. For example, the absolute grade you get from the exam (say, 85 points) is insignificant when we do not know the maximum score. But when it comes to 85/100, this price is significant. The evaluation of the model is based on the same simple logic.

I hope that in this blog your discussion of the Bayesian theorem has expanded a bit.

Source for image: https://challengersdeep.wixsite.com/website/post/od-olmayan-yerdən-tüstü-çıxmaz

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S
Sitara

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