sandbox.py

# import pandas as pd
# from sklearn.ensemble import RandomForestRegressor
# from sklearn.metrics import mean_absolute_error
# from sklearn.model_selection import train_test_split
# from sklearn.tree import DecisionTreeRegressor
# from IPython.display import display

# iowa_file_path = "./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]

# display(X.head())

# # 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))


# # Import the necessary modules and libraries
# import numpy as np
# from sklearn.tree import DecisionTreeRegressor
# import matplotlib.pyplot as plt

# # Create a random dataset
# rng = np.random.RandomState(1)
# X = np.sort(5 * rng.rand(80, 1), axis=0)
# y = np.sin(X).ravel()
# y[::5] += 3 * (0.5 - rng.rand(16))

# # Fit regression model
# regr_1 = DecisionTreeRegressor(max_depth=2)
# regr_2 = DecisionTreeRegressor(max_depth=5)
# regr_1.fit(X, y)
# regr_2.fit(X, y)

# # Predict
# X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
# y_1 = regr_1.predict(X_test)
# y_2 = regr_2.predict(X_test)

# # Plot the results
# plt.figure()
# plt.scatter(X, y, s=20, edgecolor="black", c="darkorange", label="data")
# plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
# plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
# plt.xlabel("data")
# plt.ylabel("target")
# plt.title("Decision Tree Regression")
# plt.legend()
# plt.show()