I want to plot the models prediction probabilities.
plt.scatter(y_test, prediction[:,0])
plt.xlabel("True Values")
plt.ylabel("Predictions")
plt.show()
However, I get a graph like the above. Which kind of makes sense but I want to visualize the probability distribution better. Is there a way I can do this with my actual classes being 0 or 1 and predictions between between 0 and 1.
Predicted probabilities can be utilized to visualize the model performance. True labels could be indicated using colors.
Try this example:
from sklearn.datasets import make_classification
import matplotlib.pyplot as plt
X, y = make_classification(n_samples=1000, n_features=4,
n_informative=2, n_redundant=0,
random_state=1, shuffle=False)
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression(random_state=0, solver='lbfgs', max_iter=10)
lr.fit(X, y)
prediction=lr.predict_proba(X)[:,1]
plt.figure(figsize=(15,7))
plt.hist(prediction[y==0], bins=50, label='Negatives')
plt.hist(prediction[y==1], bins=50, label='Positives', alpha=0.7, color='r')
plt.xlabel('Probability of being Positive Class', fontsize=25)
plt.ylabel('Number of records in each bucket', fontsize=25)
plt.legend(fontsize=15)
plt.tick_params(axis='both', labelsize=25, pad=5)
plt.show()
