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Reading list

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

Logistic Regression and Maximum Likelihood: Explained Simply (Part I)

A
Akash Last Updated : 06 Apr, 2022
6 min read

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

Introduction to Linear Regression

Logistic Regression
Image 1: Sales vs Budget data with a linear model representation

Linear regression is a statistical method that presumes a linear relationship between the input and the output variables. In the given example above we can see that budget is the input variable “x” and sales is the output variable “y”. In this example, the linear regression produces a linear model (blue) which presumes that there is a linear relationship between the input variable “budget” and the output variable “sales”.

Moving Beyond Linear Regression

Logistic Regression
Weights and Obesity Status of our “Friends”

Now, take a look at this example instead. What we are seeing here is that we have a bunch of people and their corresponding weights. And they are not just a bunch of ordinary people but our “friends”. You can see that we have listed the approximate weights of all the friends’ characters. Monica, Phoebe, and Rachel seem to have the weights on the lower side. Similarly, Chandler, Joey, and Ross have higher weights. Notice that there is a third column called “obese” which has two options 0 or 1. Here, 0 indicates that the corresponding person is not obese (overweight). On the other hand, 1 represents he or she is obese. So, of all the friends, Chandler and Joey seem to be slightly overweight. If we plot this information over a graph we will see a figure that looks like this.

Logistic Regression
Image 2: Plotting our Weight vs Obesity graph

Because of two classes (obese and non-obese), the graph has data points that only have values of 0 and 1. Is it possible to apply linear regression in this dataset like in the earlier example of linear regression? Well, maybe. We can make an assumption that there could be a linear relationship between the x and y-axis. However, when we try to create a linear model we can quickly find that the model could not be an accurate representation of that dataset.

Logistic Regression
Image 3: Applying linear regression in such a plot seems like a bad idea

First of all the linear model is responsible for creating too much residue/error because the distance between the predicted line and individual data points in this data set is too high. And secondly, we can also analyze that this linear equation is predicting the negative axis which doesn’t make any sense. The weight having a negative value has no meaning. Similarly, the y-axis contains only 2 classes in the form of obese and non-obese. Therefore the negative axis of “y” also holds no meaning. So it’s safe to make an assumption that linear models cannot accurately model such datasets.

The Logistic Model

Now that we are certain that we need a better option than a linear model, we introduce the concept of the logistic model. Rather than modelling y or the response directly, the logistic regression model predicts the probability that y belongs to a particular category. Therefore in our obesity example, one might predict that obesity is “yes” for any individual whose probability of weight is greater than 0.5. If we go for a more conservative approach we can also lower the threshold such that the probability of weight greater than 0.1 would be termed as obese.

In the linear model, we considered using a linear regression line to represent these probabilities in the form of the equation y = mx + b. However, in the logistic model, we use a logistic function or a sigmoid function to model our data. Mathematically the logistic model can be represented by the following equation.

<img width=”268px;” height=”81px;” src=”https://lh6.googleusercontent.com/CFfXCl_jsVE59iTQZeKU5mSfwG3qlPSXa9AW7WZoGn0G0ezmXX0q2V6PF1icxqqsLbCJ71REzS1HLaMDyczW_CMIkkxHgw0eVyBEr9CNYVw_QKHOnBdCI9F21ee29TYB3L4PRjXaYSKZ” title=”p open parentheses x close parentheses space equals space fraction numerator 1 over denominator 1 plus e to the power of negative x end exponent end fraction” alt=”px = 11+e-x”>

After a little bit of tweaking, we get this equation.

The Logistic Model

When we plot out the corresponding sigmoid graph we get something like this.

Sigmoid function graph
Image 4: The sigmoid function graph

Notice that the x-axis is spread from minus infinity to plus infinity and the y axis only contains 0 and 1. It means that the sigmoid function takes the value of the x-axis and squeezes it between 0 and 1 (doesn’t matter how large the x-axis number is). This is because the sigmoid graph creates an asymptote on the y = 1 line as the value approaches the positive infinity. Likewise, the graph also makes asymptotes on the y = 0 line. It means that the corresponding value of the function will tend to reach zero when the x-axis value tends to be minus infinity. So now as we look back on our obesity data we can intuitively say that this data would need a sigmoid function in order to represent these data points correctly. But how do we achieve this? It’s not that we are going to draw an arbitrary curve and make an assumption that it is the best fit. We didn’t follow this approach in the linear model. Remember that in the linear model we tend to find the line which has the minimum amount of error/residue. Therefore in the logistic regression as well we need to figure out a way that would minimize our error so that the data points are represented correctly by our sigmoid function. So what do you think we need to do to achieve this?

We will use the concept of maximum likelihood. But before we are jumping into the concept of likelihood I would like to introduce another concept called the “odds”. Mathematically speaking odds can be described as the ratio of the probability of an event’s occurrence over the probability of the event’s non-occurrence. Let’s say we have the sigmoid function equation like this.

Formula

After a bit of tweaking, we can get an equation that looks like this.

Sigmoid function equation | Logistic Regression

Notice that on the left-hand side of the equation we are getting the ratio of the probability of occurrence of something over the probability of non-occurrence which is also known as the odds. Suppose I tell you that the odds of Arsenal football club qualifying for the Champions League next season is 5 is to 3. It means that on 5 chances, the team would qualify whereas on three chances they would not qualify. It means that the odds are in their favour and not against them.

But let’s back off for a moment and rethink why we are calculating the odds while looking into the logistic regression. It turns out that we are deriving odds in order to derive something called log-odds or logit. Logit is nothing but the natural log of odds and it can be achieved by taking logs on both sides of the previous equation.

Formula | Logistic Regression

Now that we have figured out a way to find the log odds we can finally dive into the concept of maximum likelihood.

We will continue digging into the concepts of Maximum Likelihood in detail in the upcoming article Logistic Regression and Maximum Likelihood: Explained Simply (Part II)

End Notes

  • The linear regression cannot accurately model the classification data.
  • The idea of logistic regression is to be applied when it comes to classification data.
  • Logistic regression is used for classification problems.
  • It fits the squiggle by something called “maximum likelihood”.

 

Hope you liked my article on Linear Regression. Read more articles on the blog.

About The Author

Hi there! My name is Akash and I’ve been working as a Python developer for over 4 years now. In the course of my career, I began as a Junior Python Developer at Nepal’s biggest Job portal site. Later, I was involved in Data Science and research at Nepal’s first ride-sharing company, Tootle. Currently, I’ve been actively involved in Data Science as well as Web Development with Django.

You can find my other projects on:

https://github.com/akashadhikari

Connect me on LinkedIn

https://www.linkedin.com/in/akashadh/

Email: [email protected] | [email protected]

Image sources:

Image 1: https://www.statlearning.com/

Image 4: https://en.wikipedia.org/wiki/Logistic_function

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

blogathonlinear regressionLogistic Modelmachine learning
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Akash

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