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What is Logistic Regression in Machine Learning

Commerce has revolutionized thanks to machine learning, enabling the creation of complex apps that can tackle challenging issues. 

Classification, regression, and clustering methods help in solving issues via supervised and unsupervised machine learning models. In this article, you’ll get an in-depth overview of logistic regression machine learning, how it works, and more. So, read till the end. 

Table of Content – 

• What is Logistic Regression
• How Logistic Regression Works
• Logistic Regression Equation 
• Logistic Functions
• Types of Logistic Regression in Machine Learning
• Advantages of the Logistic Regression Algorithm
• Use Cases of Logistic Regression in Machine Learning
• Conclusion
• FAQs

What is Logistic Regression

A logistic regression algorithm in machine learning is a supervised learning algorithm that predicts the likelihood of a target variable.  Only two viable classes would exist since the target or dependent parameter is binary in nature. Simply put, the dependent variable is binary in nature, with data coded as either 1 (which represents success/yes) or 0 (which stands for failure/no). 

A logistic regression in Machine Learning model makes mathematical predictions about P(Y=1) as a function of X. One of the simplest machine learning methods, it may be applied to several categorization issues, including spam identification, diabetes prediction, cancer diagnosis, etc. 

Definition and Purpose of Logistic Regression

Logistic regression in machine learning, a supervised learning method, is used to resolve classification problems, where the objective is to estimate the likelihood that a given instance belongs to a certain class. Logistic regression is a tool used in classification algorithms. 

Regression is used because it leverages a sigmoid function to calculate the likelihood for the specified class using the output of a linear regression function as input. 

Want to learn more aspects of machine learning? Check this article on ‘What is Bagging vs. Boosting in Machine Learning?’ to continue your ML quest. 

Key Differences Between Linear Regression and Logistic Regression

We have understood what logistic regression in machine learning and purpose is. Now its Let’s explore the difference Between Linear Regression and Logistic Regression.

How Logistic Regression in Machine Learning Works

By leveraging a sigmoid function, which converts all real-valued collections of independent variables input into any value ranging between 0 and 1, the logistic regression converts the continuous value output of the linear regression function into categorical value output. 

For instance, assume the independent features to be: 

Now, assume Y, which can only take on a binary value of 0 or 1, is the dependent variable. In that case, 

Subsequently, implement the multi-linear function by using the input variable X.

Here, signifies the observation of X. Also, is the weights or Coefficient, and b is the bias term or intercept. One can denote this as the bias and weight’s dot product. 

This entire explanation above is how logistic regression in machine learning works.

Logistic Regression Equation 

The ratio of an event happening to nothing happening is called the odd. It differs from probability since probability measures the likelihood of an event happening in relation to all possible outcomes. It will be:

Now, deploy natural log on odd, which will result in the log odd being:

Subsequently, the end result of the logistic regression in machine learning equation will be: 

Logistic Function (Sigmoid Function)

One can express logistic regression algorithm in machine learning as: 

Here, the odds are p(x)/(1-p(x)). The left-hand side (LHS) is called log-odds or logit function. The ratio of the probability of win to the probability of failure is the odds. Consequently, in logistic regression, the inputs’ linear combination will go under translation to form the log(odds.

Now, here is the exact opposite of the above function:

This function called the Sigmoid function, results in an S-shaped curve. It consistently gives back a probability reading between 0 and 1. The anticipated outcomes are transformed into probabilities using the Sigmoid function. 

Any real number can be transformed by the function into a number between 0 and 1. In machine learning, we use sigmoids to convert predictions to probabilities. The sigmoid function in mathematics can be, 

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Types of Logistic Regression

Although it may forecast two more types of target variables, logistic regression in ML often refers to binary logistic regression with binary target variables. Those many criteria allow for the following categorization of a logistic regression algorithm in machine learning: 

Binomial Logistic Regression:

A dependent variable will only have one of two potential kinds in this kind of categorization, either 1 or 0. These variables can, for instance, stand for victory or defeat, yes or no, etc. 

Multinomial Logistic Regression:

The dependent variable in this sort of classification can have three or more alternative unordered types or types with no quantitative relevance. These variables could, for instance, stand for “Type A,” “Type B,” or “Type C.” 

Ordinal Logistic Regression:

In this sort of categorization, the dependent variable may have three or more possible ordered types or statistically significant types. 

Applying Steps in Logistic Regression Modeling

Here are the applying steps involved in logistic regression in machine learning modeling: 

• Step 1: Determine if the issue is a binary classification issue by identifying the dependent and independent variables. 
• Step 2: Cleaning, preprocessing, and ensuring the data are acceptable for logistic regression in machine learning modeling are all parts of data preparation. 
• Step 3: Visualize the associations between the dependent and independent variables using exploratory data analysis (EDA), and note any anomalies or outliers in the data. 
• Step 4: Feature selection involves eliminating redundant or unimportant characteristics and selecting the independent factors that significantly influence the dependent variable. 
• Step 5: Train the logistic regression model using the chosen independent variables, then calculate the model’s coefficients. 
• Step 6: Use relevant metrics, like accuracy, precision, recall, F1-score, or AUC-ROC, to assess the effectiveness of the logistic regression model. 
• Step 7: Enhance the model by changing the independent variables, including additional features, or applying regularization techniques to lessen overfitting based on the evaluation’s findings. 
• Step 8: Use a real-world scenario to implement the logistic regression model and generate predictions based on fresh data. 

Assumption in a Logistic Regression in Machine Learning 

The following presumptions concerning logistic regression algorithm in machine learning must be understood before you begin its implementation: 

• When using binary logistic regression in ML, the outcome of interest is represented by factor level 1, and the target variables must always be binary.
• The independent variables in the model must be independent to prevent multicollinearity. The model must contain relevant variables. 
• For logistic regression, a high sample size is recommended. 

• Logistic regression makes overfitting less likely, yet it still can occur in high-dimensional datasets. 
• Regularization (L1 and L2) approaches may be applied to reduce overfitting. 
• It functions well when the dataset can be linearly divided and has acceptable accuracy for many common data sets. 
• It is easier to train in, implement, and comprehend. Based on the expected parameters (trained weights), conclusions are drawn about the significance of each attribute. 
• It also states whether the association is good or negative. Logistic regression can therefore ascertain how the traits are related. 
• Logistic regression is quite effective when the dataset has characteristics that can be separated linearly. It is remarkably similar to neural networks. 
• One way to conceptualize a neural network representation is as a stacking collection of tiny logistic regression classifiers. 

Applications of Logistic Regression

Logistic regression in machine learning covers all scenarios when dividing data into numerous groups is necessary. Think about the following example: 

• Using Twitter Sentiment analysis 
• Video-based object detection 
• Prediction of diseases including diabetes, cancer, and Parkinson’s 
• Detection of fraud using credit cards 
• Spam in email or ham 
• X-ray and scan, classification, recognition, and image segmentation 
• Handwriting recognition 

Use Cases of Logistic Regression in Machine Learning

Here are the two most prominent use cases of a logistic regression algorithm in machine learning:

• Data abnormalities are indicative of fraud can be found using logistic regression, which can assist teams. 
• The likelihood of a particular population contracting a disease or other condition can be forecasted using this medical analytics method. 

Conclusion

Today, the logistic regression in machine learning has become a powerful method for binary classification. It is frequently used to model and forecast binary outcomes in many industries, including finance, social sciences, healthcare, and marketing. 

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