我試圖對引數進行超調，但在我這樣做之后，準確度分數根本沒有改變，我做錯了什么？
# Log reg
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(C=0.3326530612244898,max_iter=100,tol=0.01)
logreg.fit(X_train,y_train)
from sklearn.metrics import confusion_matrix
?
y_pred = logreg.predict(X_test)
?
print('Accuracy of log reg is: ', logreg.score(X_test,y_test))
?
confusion_matrix(y_test,y_pred)
# 0.9181286549707602 - acurracy before tunning
輸出：
Accuracy of log reg is: 0.9181286549707602
array([[ 54, 9],
[ 5, 103]])
這是我使用網格搜索簡歷：
from sklearn.model_selection import GridSearchCV
params ={'tol':[0.01,0.001,0.0001],
'max_iter':[100,150,200],
'C':np.linspace(1,20)/10}
grid_model = GridSearchCV(logreg,param_grid=params,cv=5)
grid_model_result = grid_model.fit(X_train,y_train)
print(grid_model_result.best_score_,grid_model_result.best_params_)
輸出：
0.8867405063291139 {'C': 0.3326530612244898, 'max_iter': 100, 'tol': 0.01}
uj5u.com熱心網友回復：
問題在于，在第一個塊中，您評估了模型在測驗集上的性能，而在 GridSearchCV 中，您只查看了超引數優化后訓練集上的性能。
下面的代碼顯示，當用于預測測驗集標簽時，這兩個程序在準確度 (~0.93) 方面表現同樣出色。
請注意，您可能需要考慮將超引數網格與其他求解器和更大范圍的 一起使用，max_iter因為我收到了收斂警告。
# Load packages
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
# Load the dataset and split in X and y
df = pd.read_csv('Breast_cancer_data.csv')
X = df.iloc[:, 0:5]
y = df.iloc[:, 5]
# Perform train and test split (80/20)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize a model
Log = LogisticRegression(n_jobs=-1)
# Initialize a parameter grid
params = [{'tol':[0.01,0.001,0.0001],
'max_iter':[100,150,200],
'C':np.linspace(1,20)/10}]
# Perform GridSearchCV and store the best parameters
grid_model = GridSearchCV(Log,param_grid=params,cv=5)
grid_model_result = grid_model.fit(X_train,y_train)
best_param = grid_model_result.best_params_
# This step is only to prove that both procedures actually result in the same accuracy score
Log2 = LogisticRegression(C=best_param['C'], max_iter=best_param['max_iter'], tol=best_param['tol'], n_jobs=-1)
Log2.fit(X_train, y_train)
# Perform two predictions one straight from the GridSearch and the other one with manually inputting the best params
y_pred1 = grid_model_result.best_estimator_.predict(X_test)
y_pred2 = Log2.predict(X_test)
# Compare the accuracy scores and see that both are the same
print("Accuracy:",metrics.accuracy_score(y_test, y_pred1))
print("Accuracy:",metrics.accuracy_score(y_test, y_pred2))
轉載請註明出處，本文鏈接：https://www.uj5u.com/qukuanlian/364538.html
上一篇：擬合到GridSearchCV