Machine Learning / Supervised Learning
Implementing Regression Models in Machine Learning
This tutorial will guide you through the process of implementing regression models in machine learning. We will cover basic to advanced regression techniques.
Section overview
5 resourcesExplains supervised learning techniques, algorithms, and use cases.
1. Introduction
Goal
This tutorial aims to guide you in implementing regression models using Machine Learning. We'll cover basic to advanced regression techniques with step-by-step explanations and practical examples.
Learning Objectives
By the end of this tutorial, you will be able to:
- Understand the concept of regression models in Machine Learning
- Implement basic and advanced regression models
- Understand best practices while implementing regression models
Prerequisites
Before starting this tutorial, you should have a basic understanding of:
- Python programming
- Basic Machine Learning concepts
- Libraries like NumPy, pandas, and Scikit-learn
2. Step-by-Step Guide
Regression models are supervised learning models that predict a continuous outcome variable (y) based on one or more predictor variables (x).
2.1 Linear Regression
Linear regression is the most basic type of regression. It assumes a linear relationship between the input variables (x) and the single output variable (y).
# Import necessary libraries
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# Split data into train and test datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize the Linear Regression model
lin_reg = LinearRegression()
# Train the model
lin_reg.fit(X_train, y_train)
# Make predictions
y_pred = lin_reg.predict(X_test)
2.2 Polynomial Regression
If your data points clearly cannot be represented by a linear relationship, you can use polynomial regression.
# Import necessary libraries
from sklearn.preprocessing import PolynomialFeatures
# Initialize Polynomial Features
poly_features = PolynomialFeatures(degree=2)
# Transform the features to higher degree features.
X_train_poly = poly_features.fit_transform(X_train)
# fit the transformed features to Linear Regression
poly_model = LinearRegression()
poly_model.fit(X_train_poly, y_train)
# predicting on training data-set
y_train_predicted = poly_model.predict(X_train_poly)
# predicting on test data-set
y_test_predict = poly_model.predict(poly_features.fit_transform(X_test))
3. Code Examples
3.1 Linear Regression
Let's implement a simple Linear Regression model using Scikit-learn.
# Import necessary libraries
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np
# Create a random dataset
np.random.seed(0)
x = 2 - 3 * np.random.normal(0, 1, 20)
y = x - 2 * (x ** 2) + 0.5 * (x ** 3) + np.random.normal(-3, 3, 20)
# Reshape data
x = x[:, np.newaxis]
y = y[:, np.newaxis]
# Split data into train and test datasets
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
# Initialize the Linear Regression model
model = LinearRegression()
# Train the model
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
3.2 Polynomial Regression
Let's implement a Polynomial Regression model using Scikit-learn.
# Import necessary libraries
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
# Create a Polynomial feature of degree 3
polynomial_features= PolynomialFeatures(degree=3)
# Transform the features to higher degree features.
x_poly = polynomial_features.fit_transform(x)
# fit the transformed features to Linear Regression
model = LinearRegression()
model.fit(x_poly, y)
# Visualizing the Polynomial Regression results
plt.scatter(x, y, color='red')
plt.plot(x, model.predict(polynomial_features.fit_transform(x)), color='blue')
plt.title('Predictions with Polynomial Regression')
plt.show()
4. Summary
In this tutorial, we learned about regression models and implemented Linear and Polynomial Regression models using Python and Scikit-learn. Next, you could learn about other regression models like Ridge Regression, Lasso Regression, and Logistic Regression.
5. Practice Exercises
5.1 Exercise 1
Create a dataset with a non-linear relationship and try fitting a linear regression model. Observe the result and fit a polynomial regression model to the same data and compare the results.
5.2 Exercise 2
Experiment with different degrees of polynomial regression on the same data set and observe the results.
Solutions
5.1 Solution
# Create a random dataset
np.random.seed(0)
x = 2 - 3 * np.random.normal(0, 1, 20)
y = x - 2 * (x ** 2) + 0.5 * (x ** 3) + np.random.normal(-3, 3, 20)
# Reshape data
x = x[:, np.newaxis]
y = y[:, np.newaxis]
# Initialize the Linear Regression model
model = LinearRegression()
# Train the model
model.fit(x, y)
# Make predictions
y_pred = model.predict(x)
# Create a Polynomial feature of degree 3
polynomial_features= PolynomialFeatures(degree=3)
x_poly = polynomial_features.fit_transform(x)
# fit the transformed features to Linear Regression
model_poly = LinearRegression()
model_poly.fit(x_poly, y)
# Make predictions with Polynomial Regression
y_poly_pred = model_poly.predict(x_poly)
5.2 Solution
# Create a Polynomial feature of degree 2
polynomial_features2= PolynomialFeatures(degree=2)
x_poly2 = polynomial_features2.fit_transform(x)
# fit the transformed features to Linear Regression
model_poly2 = LinearRegression()
model_poly2.fit(x_poly2, y)
# Create a Polynomial feature of degree 4
polynomial_features4= PolynomialFeatures(degree=4)
x_poly4 = polynomial_features4.fit_transform(x)
# fit the transformed features to Linear Regression
model_poly4 = LinearRegression()
model_poly4.fit(x_poly4, y)
By running these scripts, you will observe how changing the degree of the polynomial regression model affects the fit to the data.
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