kaggle官网course Machine—Learning课程Exercise全部答案

  

   

    

     

    
    

     

      # Code you have previously used to load data
     
    
   

    

     

    
    

     

      import pandas as pd
     
    
   

    

     

    
    

     

      from sklearn.metrics import mean_absolute_error
     
    
   

    

     

    
    

     

      from sklearn.model_selection import train_test_split
     
    
   

    

     

    
    

     

      from sklearn.tree import DecisionTreeRegressor
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Path of the file to read
     
    
   

    

     

    
    

     

      iowa_file_path = '../input/home-data-for-ml-course/train.csv'
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      home_data = pd.read_csv(iowa_file_path)
     
    
   

    

     

    
    

     

      # Create target object and call it y
     
    
   

    

     

    
    

     

      y = home_data.SalePrice
     
    
   

    

     

    
    

     

      # Create X
     
    
   

    

     

    
    

     

      features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
     
    
   

    

     

    
    

     

      X = home_data[features]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Split into validation and training data
     
    
   

    

     

    
    

     

      train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Specify Model
     
    
   

    

     

    
    

     

      iowa_model = DecisionTreeRegressor(random_state=1)
     
    
   

    

     

    
    

     

      # Fit Model
     
    
   

    

     

    
    

     

      iowa_model.fit(train_X, train_y)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Make validation predictions and calculate mean absolute error
     
    
   

    

     

    
    

     

      val_predictions = iowa_model.predict(val_X)
     
    
   

    

     

    
    

     

      val_mae = mean_absolute_error(val_predictions, val_y)
     
    
   

    

     

    
    

     

      print("Validation MAE when not specifying max_leaf_nodes: {:,.0f}".format(val_mae))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Using best value for max_leaf_nodes
     
    
   

    

     

    
    

     

      iowa_model = DecisionTreeRegressor(max_leaf_nodes=100, random_state=1)
     
    
   

    

     

    
    

     

      iowa_model.fit(train_X, train_y)
     
    
   

    

     

    
    

     

      val_predictions = iowa_model.predict(val_X)
     
    
   

    

     

    
    

     

      val_mae = mean_absolute_error(val_predictions, val_y)
     
    
   

    

     

    
    

     

      print("Validation MAE for best value of max_leaf_nodes: {:,.0f}".format(val_mae))
     
    
   

    

     

    
    

     

      # Define the model. Set random_state to 1
     
    
   

    

     

    
    

     

      rf_model = RandomForestRegressor(random_state=1)
     
    
   

    

     

    
    

     

      rf_model.fit(train_X, train_y)
     
    
   

    

     

    
    

     

      rf_val_predictions = rf_model.predict(val_X)
     
    
   

    

     

    
    

     

      rf_val_mae = mean_absolute_error(rf_val_predictions, val_y)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print("Validation MAE for Random Forest Model: {:,.0f}".format(rf_val_mae))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Set up code checking
     
    
   

    

     

    
    

     

      from learntools.core import binder
     
    
   

    

     

    
    

     

      binder.bind(globals())
     
    
   

    

     

    
    

     

      from learntools.machine_learning.ex6 import *
     
    
   

    

     

    
    

     

      print("\nSetup complete")
     
    
  
 

文章来源: blog.csdn.net，作者：小小谢先生，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/xiewenrui1996/article/details/103478207