8. Parameter Tuning [파이썬으로 배우는 데이터 사이언스]

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Parameter Tuning

 

 

 

 

 

Contents: 

 

1. Grid Search 

2. Random Search 

 

 

 

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1. Grid Search 

 

 

import numpy as np 

from sklearn.linear_model import RidgeClassifier 

from sklearn.model_selection import GridSearchCV 

alphas=np.array([1, 0.1, 0.01, 0.001, 0]) 

param_grid=dict(alpha=alphas) 

model=RidgeClassifier() 

grid=GridSearchCV(estimator=model, param_grid=param_grid, cv=3) 

grid.fit(X,y) 

print(grid.best_score_) 

print(grid.best_estimator_.alpha) 

 

 

 

 

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2. Random Search 

 

 

from scipy.stats import uniform 

from sklearn.linear_model import RidgeClassifier 

from sklearn.model_selection import RandomizedSearchCV 

 

param_grid={'alpha'=uniform()} 

model=RidgeClassifier() 

rsearch=GridSearchCV(estimator=model, param_distributions=param_grid, n_iter=100, cv=3, random_state=6) 

rsearch.fit(X,y) 

print(rsearch.best_score_) 

print(rsearch.best_estimator_.alpha) 

 

 

 

 

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밑바닥부터 시작하는 데이터 러닝

* 해당 게시물은 첫번째 배너 및 출처를 첨부하면 상업적 용도를 제외하고 사용하실수 있습니다.