You've built your first model, and now it's time to optimize the size of the tree to make better predictions. Run this cell to set up your coding environment where the previous step left off.
# 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: {:,.0f}".format(val_mae))
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex5 import *
print("\nSetup complete")
You could write the function get_mae yourself. For now, we'll supply it. This is the same function you read about in the previous lesson. Just run the cell below.
def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(train_X, train_y)
preds_val = model.predict(val_X)
mae = mean_absolute_error(val_y, preds_val)
return(mae)
Write a loop that tries the following values for max_leaf_nodes from a set of possible values.
Call the get_mae function on each value of max_leaf_nodes. Store the output in some way that allows you to select the value of max_leaf_nodes that gives the most accurate model on your data.
candidate_max_leaf_nodes = [5, 25, 50, 100, 250, 500]
# Write loop to find the ideal tree size from candidate_max_leaf_nodes
for max_leaf_nodes in candidate_max_leaf_nodes:
my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)
print("Max leaf nodes: %d \t\t Mean Absolute Error: %d" %(max_leaf_nodes, my_mae))
# Store the best value of max_leaf_nodes (it will be either 5, 25, 50, 100, 250 or 500)
scores = {leaf_size: get_mae(leaf_size, train_X, val_X, train_y, val_y) for leaf_size in candidate_max_leaf_nodes}
print("\n",scores)
best_tree_size = min(scores, key=scores.get)
print("\n", best_tree_size, scores[best_tree_size])
step_1.check()
# The lines below will show you a hint or the solution.
step_1.hint()
step_1.solution()
You know the best tree size. If you were going to deploy this model in practice, you would make it even more accurate by using all of the data and keeping that tree size. That is, you don't need to hold out the validation data now that you've made all your modeling decisions.
# Fit the model with best_tree_size. Fill in argument to make optimal size
final_model = DecisionTreeRegressor(max_leaf_nodes=best_tree_size, random_state=0)
# fit the final model
final_model.fit(X, y)
step_2.check()
step_2.hint()
step_2.solution()
You've tuned this model and improved your results. But we are still using Decision Tree models, which are not very sophisticated by modern machine learning standards. In the next step you will learn to use Random Forests to improve your models even more. Random Forests are based on decision trees, but they typically give you much better predictions.