An Introduction to Scikit-Learn: Machine Learning in Python

28 An Introduction to Scikit-Learn: Machine Learning in Python のメモ

1. from sklearn.datasets import load_iris
   import kagglehub
   path = kagglehub.dataset_download("uciml/red-wine-quality-cortez-et-al-2009")
   import pandas as pd
   import numpy as np
   from sklearn.model_selection import train_test_split
   from sklearn.externals import joblib
   import joblib
   from sklearn import preprocessing
   from sklearn.ensemble import RandomForestRegressor
   from sklearn.pipeline import make_pipeline
   from sklearn.model_selection import GridSearchCV
   from sklearn.metrics import mean_squared_error, r2_score
   
   wine = pd.read_csv('winequality-red.csv')
   wine = pd.read_csv(path+'/winequality-red.csv')
   

2. # Declare hyperparameters to tune
   hyperparameters = { 'randomforestregressor__max_features' : ['auto','sqrt'],
   hyperparameters = { 'randomforestregressor__max_features' : ['sqrt', 'log2'],
   'randomforestregressor__max_depth': [None, 1, 2, 4]}