Logistic Regression for Beginners

This article “Logistic Regression for Beginners” targets beginners and provides a Python code snippet as well.

We use Logistic regression for predicting binary outcomes, such as the likelihood of an event occurring or the probability of a person belonging to a certain class or category. We use this technique widely in machine learning and data analysis. It is particularly useful for classification tasks where the goal is to predict a discrete label (e.g., spam or not spam, malignant or benign).

Logistic Regression

Logistic regression is a type of regression analysis. It models the probability of an event occurring as a function of one or more independent variables. Unlike linear regression, which is used to predict continuous variables, logistic regression is used to predict the probability of a binary outcome. We represent this probability by a value between 0 and 1, where 0 indicates that the event is unlikely to occur and 1 indicates that it is very likely to occur.

To fit a logistic regression model, we need a set of data points with both independent and dependent variables. The independent variables, are also known as predictors or features. We use them to predict the dependent variable, which is the binary outcome. The goal is to find the line of best fit that maximizes the probability of correctly classifying the data points.

Logistic regression can be used for both simple logistic regression, where there is only one independent variable, and multiple logistic regression, where there are multiple independent variables. In multiple logistic regression, the model takes the form of a logistic equation with a coefficient for each independent variable.

One of the main advantages of logistic regression is that it is easy to interpret and implement. It also has a low risk of overfitting, which means that it is less likely to make overly complex predictions that are not generalizable to new data. However, it is important to note that logistic regression assumes that the relationship between the dependent and independent variables is linear, which may not always be the case in real-world data. This section of the article “Logistic Regression for Beginners” ends here.

Logistic Regression: Python Example

To use logistic regression in Python, you will need to have the scikit-learn library installed. You can install scikit-learn by running the following command:

pip install scikit-learn

Once you have installed scikit-learn, you can import the logistic regression model from the linear_model module. Here is an example of how to use logistic regression in Python:

from sklearn.linear_model import LogisticRegression

# Load the data
X = [[0, 1], [5, 1], [15, 2], [25, 5], [35, 11], [45, 15], [55, 34], [60, 35]]
y = [0, 0, 1, 0, 1, 0, 1, 1]
X_test = [[0, 1], [10, 2], [20, 5], [30, 11], [40, 15], [50, 34]]

# Create the logistic regression model
model = LogisticRegression()

# Train the model using the training data
model.fit(X, y)

# Predict the values for the test data
y_pred = model.predict(X_test)

# Print the predictions
print(y_pred)

The code is performing logistic regression on a small dataset with 2 features and binary classification. The dataset is split into training data (X and y) and test data (X_test).

A logistic regression model is created using the LogisticRegression() function from the scikit-learn library. The model is then trained using the training data with the fit() method.

Finally, the model is used to predict the outcomes of the test data using the predict() method, and the predictions are printed to the console using the print() function.

This code will create a logistic regression model, fit the model to the training data, and then use the model to predict the values for the test data. The output of this code will be an array of predicted values for the test data, where 0 indicates that the event is unlikely to occur and 1 indicates that it is very likely to occur.

In addition to predicting binary outcomes, logistic regression can also be used to predict the probability of an event occurring. To get the probability of an event occurring, you can use the predict_proba() method instead of the predict() method. For example:

# Predict the probability of an event occurring
y_prob = model.predict_proba(X_test)

# Print the probabilities
print(y_prob)

The code is predicting the probabilities of an event occurring using the logistic regression model created earlier.

The predict_proba() method from the logistic regression model is used to predict the probabilities of each test sample belonging to the positive and negative classes. The output is an array with two columns representing the probability of the sample belonging to each class.

The probabilities are printed to the console using the print() function.

Conclusion

Logistic regression is a powerful tool for classification tasks in data science. It is relatively simple to implement and interpret. We use it to predict binary outcomes based on one or more independent variables.

Hope this article “Logistic Regression for Beginners” helped you in gaining a new perspective. I would recommend this article for further reading.

You may also like:

Check out the table of contents for Product Management and Data Science to explore those topics.

Curious about how product managers can utilize Bhagwad Gita’s principles to tackle difficulties? Give this super short book a shot. This will certainly support my work.

Leave a Reply