INTRO to Machine Learning

来源：Introduction to Machine Learning
2024-08-29@isSeymour

1. 训练与验证

1
2
3
4
5
# Set up filepaths
import os
if not os.path.exists("../input/train.csv"):
    os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")  
    os.symlink("../input/home-data-for-ml-course/test.csv", "../input/test.csv") 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
# Import helpful libraries
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

# Load the data, and separate the target
iowa_file_path = '../input/train.csv'
home_data = pd.read_csv(iowa_file_path)
y = home_data.SalePrice

# Create X (After completing the exercise, you can return to modify this line!)
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']

# Select columns corresponding to features, and preview the data
X = home_data[features]
print(X.head())

# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

# Define a random forest model
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))
   LotArea  YearBuilt  1stFlrSF  2ndFlrSF  FullBath  BedroomAbvGr  \
0     8450       2003       856       854         2             3   
1     9600       1976      1262         0         2             3   
2    11250       2001       920       866         2             3   
3     9550       1915       961       756         1             3   
4    14260       2000      1145      1053         2             4   

   TotRmsAbvGrd  
0             8  
1             6  
2             6  
3             7  
4             9  
Validation MAE for Random Forest Model: 21,857

2. 产出模型

1
2
3
4
5
# To improve accuracy, create a new Random Forest model which you will train on all training data
rf_model_on_full_data = RandomForestRegressor(random_state=1)

# fit rf_model_on_full_data on all data from the training data
rf_model_on_full_data.fit(X, y)
RandomForestRegressor(random_state=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

3. 模型预测

1
2
3
4
5
6
7
8
9
10
11
12
# path to file you will use for predictions
test_data_path = '../input/test.csv'

# read test data file using pandas
test_data = pd.read_csv(test_data_path)

# create test_X which comes from test_data but includes only the columns you used for prediction.
# The list of columns is stored in a variable called features
test_X = test_data[features]

# make predictions which we will submit. 
test_preds = rf_model_on_full_data.predict(test_X)

4. 导出结果

1
2
3
4
# Run the code to save predictions in the format used for competition scoring
output = pd.DataFrame({'Id': test_data.Id,
                       'SalePrice': test_preds})
output.to_csv('submission.csv', index=False)