机器学习-实践-准备工作

1. sklearn数据库

load_boston([return_X_y])	Load and return the boston house-prices dataset (regression).
load_diabetes([return_X_y])	Load and return the diabetes dataset (regression).
load_linnerud([return_X_y])	Load and return the linnerud dataset (multivariate regression).

load_iris([return_X_y])	Load and return the iris dataset (classification).
load_digits([n_class, return_X_y])	Load and return the digits dataset (classification).
load_wine([return_X_y])	Load and return the wine dataset (classification).
load_breast_cancer([return_X_y])	Load and return the breast cancer wisconsin dataset (classification).

2. 评测标准

#--------------------------------------------------------------------------- # 
# Classification	 	 
‘accuracy’	metrics.accuracy_score	 
‘balanced_accuracy’	metrics.balanced_accuracy_score	for binary targets
‘average_precision’	metrics.average_precision_score	 
‘brier_score_loss’	metrics.brier_score_loss	 
‘f1’	metrics.f1_score	for binary targets
‘f1_micro’	metrics.f1_score	micro-averaged
‘f1_macro’	metrics.f1_score	macro-averaged
‘f1_weighted’	metrics.f1_score	weighted average
‘f1_samples’	metrics.f1_score	by multilabel sample
‘neg_log_loss’	metrics.log_loss	requires predict_proba support
‘precision’ etc.	metrics.precision_score	suffixes apply as with ‘f1’
‘recall’ etc.	metrics.recall_score	suffixes apply as with ‘f1’
‘roc_auc’	metrics.roc_auc_score	 
# --------------------------------------------------------------------------- #
# Clustering	 	 
‘adjusted_mutual_info_score’	metrics.adjusted_mutual_info_score	 
‘adjusted_rand_score’	metrics.adjusted_rand_score	 
‘completeness_score’	metrics.completeness_score	 
‘fowlkes_mallows_score’	metrics.fowlkes_mallows_score	 
‘homogeneity_score’	metrics.homogeneity_score	 
‘mutual_info_score’	metrics.mutual_info_score	 
‘normalized_mutual_info_score’	metrics.normalized_mutual_info_score	 
‘v_measure_score’	metrics.v_measure_score	 
# --------------------------------------------------------------------------- #
# Regression	 	 
‘explained_variance’	metrics.explained_variance_score	 
‘neg_mean_absolute_error’	metrics.mean_absolute_error	 
‘neg_mean_squared_error’	metrics.mean_squared_error	 
‘neg_mean_squared_log_error’	metrics.mean_squared_log_error	 
‘neg_median_absolute_error’	metrics.median_absolute_error	 
‘r2’	metrics.r2_score	 
# --------------------------------------------------------------------------- #

3. 调参-gridsearch

4. 特征工程

https://www.cnblogs.com/stevenlk/p/6543628.html