Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to graph grid scores from GridSearchCV** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to graph grid scores from GridSearchCV?

**How to graph grid scores from GridSearchCV?**The order that the parameter grid is traversed is deterministic, such that it can be reshaped and plotted straightforwardly. Something like this:

`scores = [entry.mean_validation_score for entry in grid.grid_scores_]`

**graph grid scores from GridSearchCV**The order that the parameter grid is traversed is deterministic, such that it can be reshaped and plotted straightforwardly. Something like this:

`scores = [entry.mean_validation_score for entry in grid.grid_scores_]`

## Method 1

from sklearn.svm import SVC from sklearn.grid_search import GridSearchCV from sklearn import datasets import matplotlib.pyplot as plt import seaborn as sns import numpy as np digits = datasets.load_digits() X = digits.data y = digits.target clf_ = SVC(kernel='rbf') Cs = [1, 10, 100, 1000] Gammas = [1e-3, 1e-4] clf = GridSearchCV(clf_, dict(C=Cs, gamma=Gammas), cv=2, pre_dispatch='1*n_jobs', n_jobs=1) clf.fit(X, y) scores = [x[1] for x in clf.grid_scores_] scores = np.array(scores).reshape(len(Cs), len(Gammas)) for ind, i in enumerate(Cs): plt.plot(Gammas, scores[ind], label='C: ' + str(i)) plt.legend() plt.xlabel('Gamma') plt.ylabel('Mean score') plt.show()

- Code is based on this.
- Only puzzling part: will sklearn always respect the order of C & Gamma -> official example uses this “ordering”

Output:

## Method 2

The order that the parameter grid is traversed is deterministic, such that it can be reshaped and plotted straightforwardly. Something like this:

scores = [entry.mean_validation_score for entry in grid.grid_scores_] # the shape is according to the alphabetical order of the parameters in the grid scores = np.array(scores).reshape(len(C_range), len(gamma_range)) for c_scores in scores: plt.plot(gamma_range, c_scores, '-')

**Conclusion**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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