An example showing the plot_learning_curve method used by a scikit-learn classifier

In this example, we'll be plotting the learning curve of a RandomForestClassifier to see both training and cross-validation scores. We'll call the scikitplot.estimators.plot_learning_curve method, and supply our classifier and our features and labels.



In [1]:

    
from __future__ import absolute_import
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer as load_data

# Import scikit-plot
import scikitplot as skplt

%pylab inline
pylab.rcParams['figure.figsize'] = (14, 14)









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
# Load the data
X, y = load_data(return_X_y=True)

# Create classifier instance
rf = RandomForestClassifier()



In [3]:

    
# Plot!
skplt.estimators.plot_learning_curve(rf, X, y)
plt.show()