Linear Models

What are Linear Models?

Linear models are statistical models that assume a linear relationship between the independent variables (features) and the dependent variable (target). They are widely used for both regression and classification tasks.

Key Concepts

• Linear Regression: Predict a continuous target variable based on a linear relationship with one or more features.
• Logistic Regression: Predict a categorical target variable based on a linear combination of features.
• Regularization: Techniques like L1 and L2 regularization to prevent overfitting.
• Evaluation Metrics: Metrics like MSE, RMSE, accuracy, and precision-recall to assess model performance.

Getting Started with Scikit-Learn

Scikit-learn provides a simple and efficient way to build and train linear models. Here's a basic example:

                
import sklearn.linear_model
import numpy as np

# Create a linear regression model
model = sklearn.linear_model.LinearRegression()

# Train the model
X = np.array([[1], [2], [3]]) # Features
y = np.array([2, 4, 5])       # Target
model.fit(X, y)

# Make a prediction
prediction = model.predict([[4]])
print(prediction)
                
              

Next Steps

Explore the following topics to deepen your understanding of linear models:

• Linear Regression Tutorial
• Logistic Regression Tutorial
• Regularization Techniques