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<<< Logistic Regression Algorithm

  1. Overview
  2. Classification
  3. Regression
  4. Advantages
  5. Disadvantages
  6. Logistic Regression Complexity
  7. Tuning Logistic Regression
  8. Who Invented Logistic Regression?
  9. Logistic Regression Example 1

Logistic Regression Classification

Logistic Regression is a commonly used machine learning algorithm for classification problems. It can be conveniently implemented using Scikit-Learn and Python.

LogisticRegression is the class name used for creating Logistic Regression models and it belongs to linear_model group in sklearn module.

How to Construct?

1- LogisticRegression

We can implement Logistic Regression algorithm simply using Scikit-Learn’s LogisticRegression class to create a machine learning model. LogisticRegression class belongs to linear_model module and can be used to construct a model.

a) Creating LogisticRegression Model:

You can create a LogisticRegression model using Python and Scikit-Learn. Here is a simple Python code to do that:

from sklearn import linear_model
LogR = linear_model.LogisticRegression()

b) Training LogisticRegression Model:

Once we have a Logistic Regression model, we can train it as below, so we can use it for predictions:

LogR.fit(X_train, y_train)

c) Predicting with LogisticRegression Model:

After Logistic Model is trained it will be ready for inference or in other words process of making predictions. Logistic Regression is used solely in classification predictions. So we can only use it in classification problems.

Most other supervised machine learning algorithms also have regression versions and are capable of solving regression problems by predicting continuous values. Another exception that can only do classification similar to Logistic Regression is Naive Bayes algorithm.

Here is a basic Python code that shows how prediction can be done using a trained Logistic Regression model:

yhat = LogR.predict(X_test)

Remember, Machine Learning usually is an iterative process which requires going back and forth to either adjust the data, adjust the process or adjust the model in use. Logistic Regression offers plenty of optimization opportunities like most other machine learning algorithms. 

You can have better control in the performance, accuracy, efficiency and the general outcome of your Logistic Regression implementation by adjusting its hyperparameters. We have prepared a tutorial that shows how Logistic Regression models can be tuned which can be found below:

Logistic Regression Visualization and Practical Implementation

2- Logistic Regression Example

You can see the tutorial below for a practical example of Logistic Regression algorithm and related machine learning visualizations: