kfold_cross_validation.py
# -*- coding: utf-8 -*- """CrossValidation-ScikitLearn_PythonCodeTutorial.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/15FFmKBlvdAFCP4-Ka2SoFsWC93PjdxJH """ # Load libraries from sklearn import datasets from sklearn import metrics from sklearn.model_selection import KFold, cross_val_score from sklearn.pipeline import make_pipeline from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler # digits dataset loading digits = datasets.load_digits() # Create features matrix features = digits.data # Create target vector target = digits.target # standardization standard_scaler = StandardScaler() # logistic regression creation logit = LogisticRegression() # pipeline creation for standardization and performing logistic regression pipeline = make_pipeline(standard_scaler, logit) # perform k-Fold cross-validation kf = KFold(n_splits=11, shuffle=True, random_state=2) # k-fold cross-validation conduction cv_results = cross_val_score(pipeline, # Pipeline features, # Feature matrix target, # Target vector cv=kf, # Cross-validation technique scoring="accuracy", # Loss function n_jobs=-1) # Use all CPU cores # View score for all 11 folds cv_results # Calculate mean cv_results.mean()
Join 50,000+ Python Programmers & Enthusiasts like you!
Spring Promotion Special! Get our The Python Code eBook Bundle at 40% off. Limited time only!
$122.00 $73.20