Skip to main content

Evaluation Performance measure of Regression models

 

Evaluation Performance measure of Regression models

You need to read CSV 

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import (mean_absolute_error, mean_squared_error, r2_score,
                             accuracy_score, precision_score, recall_score, f1_score,
                             confusion_matrix, roc_curve, auc, RocCurveDisplay)
import matplotlib.pyplot as plt

# Assuming you have a dataset loaded as `data`
# For simplicity, let's assume 'X' are the features and 'y' is the target

# Splitting the dataset into train and test sets
X = data.drop(columns=['target'])
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

### Linear Regression ###
linear_model = LinearRegression()
linear_model.fit(X_train, y_train)
y_pred_linear = linear_model.predict(X_test)

# Performance metrics for Linear Regression
mae_linear = mean_absolute_error(y_test, y_pred_linear)
mse_linear = mean_squared_error(y_test, y_pred_linear)
rmse_linear = np.sqrt(mse_linear)
r2_linear = r2_score(y_test, y_pred_linear)

print("Linear Regression Metrics:")
print(f"MAE: {mae_linear}")
print(f"MSE: {mse_linear}")
print(f"RMSE: {rmse_linear}")
print(f"R²: {r2_linear}")

### Quadratic Regression (Polynomial Regression) ###
poly = PolynomialFeatures(degree=2)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.transform(X_test)

quadratic_model = LinearRegression()
quadratic_model.fit(X_train_poly, y_train)
y_pred_quad = quadratic_model.predict(X_test_poly)

# Performance metrics for Quadratic Regression
mae_quad = mean_absolute_error(y_test, y_pred_quad)
mse_quad = mean_squared_error(y_test, y_pred_quad)
rmse_quad = np.sqrt(mse_quad)
r2_quad = r2_score(y_test, y_pred_quad)

print("\nQuadratic Regression Metrics:")
print(f"MAE: {mae_quad}")
print(f"MSE: {mse_quad}")
print(f"RMSE: {rmse_quad}")
print(f"R²: {r2_quad}")

### Logistic Regression ###
logistic_model = LogisticRegression()
logistic_model.fit(X_train, y_train)
y_pred_logistic = logistic_model.predict(X_test)

# Performance metrics for Logistic Regression
accuracy = accuracy_score(y_test, y_pred_logistic)
precision = precision_score(y_test, y_pred_logistic)
recall = recall_score(y_test, y_pred_logistic)
f1 = f1_score(y_test, y_pred_logistic)
conf_matrix = confusion_matrix(y_test, y_pred_logistic)

print("\nLogistic Regression Metrics:")
print(f"Accuracy: {accuracy}")
print(f"Precision: {precision}")
print(f"Recall: {recall}")
print(f"F1 Score: {f1}")
print("Confusion Matrix:")
print(conf_matrix)

### ROC Curve for Logistic Regression ###
y_pred_proba = logistic_model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
roc_auc = auc(fpr, tpr)

plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()

Comments

Post a Comment

Popular posts from this blog

ML Lab Questions

1. Using matplotlib and seaborn to perform data visualization on the standard dataset a. Perform the preprocessing b. Print the no of rows and columns c. Plot box plot d. Heat map e. Scatter plot f. Bubble chart g. Area chart 2. Build a Linear Regression model using Gradient Descent methods in Python for a wine data set 3. Build a Linear Regression model using an ordinary least-squared model in Python for a wine data set  4. Implement quadratic Regression for the wine dataset 5. Implement Logistic Regression for the wine data set 6. Implement classification using SVM for Iris Dataset 7. Implement Decision-tree learning for the Tip Dataset 8. Implement Bagging using Random Forests  9.  Implement K-means Clustering    10.  Implement DBSCAN clustering  11.  Implement the Gaussian Mixture Model  12. Solve the curse of Dimensionality by implementing the PCA algorithm on a high-dimensional 13. Comparison of Classification algorithms  14. Compa...
Post a Comment
Read more

DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular density-based clustering algorithm that groups data points based on their density in feature space. It’s beneficial for datasets with clusters of varying shapes, sizes, and densities, and can identify noise or outliers. Step 1: Initialize Parameters Define two important parameters: Epsilon (ε) : The maximum distance between two points for them to be considered neighbors. Minimum Points (minPts) : The minimum number of points required in an ε-radius neighborhood for a point to be considered a core point. Step 2: Label Each Point as Core, Border, or Noise For each data point P P P in the dataset: Find all points within the ε radius of P P P (the ε-neighborhood of P P P ). Core Point : If P P P has at least minPts points within its ε-neighborhood, it’s marked as a core point. Border Point : If P P P has fewer than minPts points in its ε-neighborhood but is within the ε-neighborhood of a core point, it’...
Post a Comment
Read more

Gaussian Mixture Model

A Gaussian Mixture Model (GMM) is a probabilistic model used for clustering and density estimation. It assumes that data is generated from a mixture of several Gaussian distributions, each representing a cluster within the dataset. Unlike K-means, which assigns data points to the nearest cluster centroid deterministically, GMM considers each data point as belonging to each cluster with a certain probability, allowing for soft clustering. GMM is ideal when: Clusters have elliptical shapes or different spreads : GMM captures varying shapes and densities, unlike K-means, which assumes clusters are spherical. Soft clustering is preferred : If you want to know the probability of a data point belonging to each cluster (not a hard assignment). Data has overlapping clusters : GMM allows a point to belong partially to multiple clusters, which is helpful when clusters have significant overlap. Applications of GMM Image Segmentation : Used to segment images into regions, where each region can be...
Post a Comment
Read more