Regression Analysis

Regression analysis is a fundamental statistical method used to understand the relationship between variables. In the context of AI Toolkit, regression analysis can be a powerful tool for predictive modeling and decision-making.

Common Types of Regression

  • Linear Regression: Used to model the linear relationship between the independent and dependent variables.
  • Logistic Regression: A special type of regression used for binary classification problems.
  • Multiple Regression: Expands linear regression to include multiple independent variables.

Getting Started

To start using regression analysis in AI Toolkit, follow these steps:

  1. Install AI Toolkit: Ensure that you have AI Toolkit installed on your system.
  2. Import Necessary Modules: Import the necessary modules for regression analysis.
  3. Load Data: Load your dataset into AI Toolkit.
  4. Split Data: Split your data into training and testing sets.
  5. Train Model: Train a regression model using the training data.
  6. Evaluate Model: Evaluate the model's performance using the testing data.

Example

Here's a simple example of linear regression using AI Toolkit:


from aitoolkit.regression import LinearRegression

# Load data
X = [[1, 2], [2, 3], [3, 4]]
y = [2, 4, 6]

# Train model
model = LinearRegression()
model.fit(X, y)

# Predict
prediction = model.predict([[4, 5]])
print(prediction)

Further Reading

For more detailed information on regression analysis in AI Toolkit, check out our API Documentation.

Visualization

Understanding the relationship between variables can be visualized using various plots. Below is an example of a scatter plot showing a linear relationship:

Linear Regression Plot