AUC-ROC analysis with Python

AUC-ROC analysis is a commonly used method to evaluate the performance of binary classification models. It measures the ability of the model to distinguish between positive and negative classes. Step 1: Import required libraries and load the data 
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt

# Load your data here

Step 2: Fit your model and make predictions 
# Fit your model and make predictions
# Example:
# model.fit(X_train, y_train)
# y_pred = model.predict_proba(X_test)[:,1]

Step 3: Calculate AUC-ROC score and plot the ROC curve 
# Calculate AUC-ROC score
auc_score = roc_auc_score(y_test, y_pred)

# Plot ROC curve
fpr, tpr, thresholds = roc_curve(y_test, y_pred)
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score)
plt.plot([0, 1], [0, 1], 'k--')
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

Seven common machine learning evaluation metrics

Seven common machine learning evaluation metrics are essential in assessing the performance of machine learning models. These metrics include accuracy, precision, recall, F1 score, ROC-AUC, mean squared error, and R-squared. Accuracy measures the percentage of correctly classified instances, precision measures the ratio of correctly predicted positive observations to the total predicted positive observations, recall measures the ratio of correctly predicted positive observations to the total actual positive observations, F1 score is the harmonic mean of precision and recall, ROC-AUC calculates the area under the receiver operating characteristic curve, mean squared error measures the average of the squares of the errors, and R-squared represents the proportion of the variance in the dependent variable that is predictable from the independent variables. 1. Accuracy: Accuracy is the most basic evaluation metric, representing the ratio of correctly predicted instances to the total instan
Read more

How does Python handle dynamic typing?

In the first example, we are going to demonstrate how Python handles dynamic typing. Here is the code: x = 5 print(x) x = "hello" print(x) Step 1: We initialize a variable x with the value 5. Step 2: We print the value of x , which is 5. Step 3: We reassign the variable x with the string "hello". Step 4: We print the value of x again, which is now "hello". In Python, variables can hold different data types and can be reassigned at any time, showcasing the language's dynamic typing capabilities. In the second example, we are going to further explore dynamic typing in Python. Here is the code: x = 10 y = "20" print(x + y) Step 1: We initialize a variable x with the integer value 10. Step 2: We initialize a variable y with the string value "20". Step 3: We attempt to add x and y together. Step 4: An error will occur because Python does not automatically convert data types in this context. Python's dynam
Read more