41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
|
"""
|
||
|
|
==========================
|
||
|
|
Plotting Validation Curves
|
||
|
|
==========================
|
||
|
|
|
||
|
|
In this plot you can see the training scores and validation scores of an SVM
|
||
|
|
for different values of the kernel parameter gamma. For very low values of
|
||
|
|
gamma, you can see that both the training score and the validation score are
|
||
|
|
low. This is called underfitting. Medium values of gamma will result in high
|
||
|
|
values for both scores, i.e. the classifier is performing fairly well. If gamma
|
||
|
|
is too high, the classifier will overfit, which means that the training score
|
||
|
|
is good but the validation score is poor.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from sklearn.datasets import load_digits
|
||
|
|
from sklearn.model_selection import ValidationCurveDisplay
|
||
|
|
from sklearn.svm import SVC
|
||
|
|
|
||
|
|
X, y = load_digits(return_X_y=True)
|
||
|
|
subset_mask = np.isin(y, [1, 2]) # binary classification: 1 vs 2
|
||
|
|
X, y = X[subset_mask], y[subset_mask]
|
||
|
|
|
||
|
|
disp = ValidationCurveDisplay.from_estimator(
|
||
|
|
SVC(),
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
param_name="gamma",
|
||
|
|
param_range=np.logspace(-6, -1, 5),
|
||
|
|
score_type="both",
|
||
|
|
n_jobs=2,
|
||
|
|
score_name="Accuracy",
|
||
|
|
)
|
||
|
|
disp.ax_.set_title("Validation Curve for SVM with an RBF kernel")
|
||
|
|
disp.ax_.set_xlabel(r"gamma (inverse radius of the RBF kernel)")
|
||
|
|
disp.ax_.set_ylim(0.0, 1.1)
|
||
|
|
plt.show()
|