Challenge: Tuning Hyperparameters with RandomizedSearchCV

The idea behind RandomizedSearchCV is that it works the same as GridSearchCV, but instead of trying all the combinations, it tries a randomly sampled subset.

For example, this param_grid will have 100 combinations:


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param_grid = {'n_neighbors': [1, 3, 5, 7, 9, 12, 15, 17, 20, 25],
                         'weights': ['distance', 'uniform'],
                         'p': [1, 2, 3, 4, 5]}

The GridSearchCV would try all of them, which is time-consuming. With RandomizedSearchCV, you can try only a randomly chosen subset of, say, 20 combinations. It usually leads to a little worse result, but works much faster.

You can control the number of combinations to be tested using the n_iter argument (set to 10 by default). Apart from that, working with it is the same as with GridSearchCV.

Tâche

Swipe to start coding

Your task is to build GridSearchCV and RandomizedSearchCV with 20 combinations and compare the results.

Initialize the RandomizedSearchCV object. Pass the parameters grid and set the number of combinations to 20.
Initialize the GridSearchCV object.
Train both GridSearchCV and RandomizedSearchCV objects.
Print the best estimator of grid.
Print the best score of randomized.

Solution

import pandas as pd

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import RandomizedSearchCV, GridSearchCV

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_pipelined.csv')

# Assign X, y variables (X is already preprocessed and y is already encoded)

X, y = df.drop('species', axis=1), df['species']

# Create the param_grid and initialize a model

param_grid = {'n_neighbors': [1, 3, 5, 7, 9, 12, 15, 17, 20, 25],

'weights': ['distance', 'uniform'],

'p': [1, 2, 3, 4, 5]

}

model = KNeighborsClassifier()

# Initialize RandomizedSearchCV and GridSearchCV

randomized = RandomizedSearchCV(model, param_grid, n_iter=20)

grid = GridSearchCV(model, param_grid)

# Train the GridSearchCV object. During training it finds the best parameters

grid.fit(X, y)

randomized.fit(X, y)

# Print the best estimator and its cross-validation score

print('GridSearchCV:')

print(grid.best_estimator_)

print(grid.best_score_)

print('RandomizedSearchCV:')

print(randomized.best_estimator_)

print(randomized.best_score_)

Tout était clair ?

Merci pour vos commentaires !

Section 4. Chapitre 8

single

import pandas as pd

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import RandomizedSearchCV, GridSearchCV

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_pipelined.csv')

# Assign X, y variables (X is already preprocessed and y is already encoded)

X, y = df.drop('species', axis=1), df['species']

# Create the param_grid and initialize a model

param_grid = {'n_neighbors': [1, 3, 5, 7, 9, 12, 15, 17, 20, 25],

'weights': ['distance', 'uniform'],

'p': [1, 2, 3, 4, 5]

}

model = KNeighborsClassifier()

# Initialize RandomizedSearchCV and GridSearchCV

randomized = ___

grid = ___

# Train the GridSearchCV object. During training it finds the best parameters

grid.___

randomized.___

# Print the best estimator and its cross-validation score

print('GridSearchCV:')

print(grid.___)

print(grid.best_score_)

print('RandomizedSearchCV:')

print(randomized.best_estimator_)

print(randomized.___)

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