One way to create a multivariate model is to: 1. Rank the independent variables by correlation, then create a linear model using the independent variable with the highest correlation. Measure the training and testing accuracy. 2. Add in the independent variable with the next highest correlation and create a new linear model. Measure the training and testing accuracy. 3. Stop when either accuracy score levels off or goes down.follow the steps above to create models with one, two and three independent variables, printing the training and testing accuracy each time. Note that you have to run _train_test_split_ for each model. Set the _random_state_ parameter in _train_test_split_ to 0 each time.
时间: 2024-04-10 11:34:40 浏览: 20
Sure! To create a multivariate model using the steps you mentioned, you can follow the code snippet below:
```python
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
# Step 1: Create a linear model using the independent variable with the highest correlation
# Assuming you have a pandas DataFrame called 'data' with independent variables 'X1', 'X2', 'X3', ..., and dependent variable 'y'
X = data[['X1']] # Select the independent variable with the highest correlation
y = data['y']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)
train_accuracy = model.score(X_train, y_train)
test_accuracy = model.score(X_test, y_test)
print("Model 1 - Independent Variable: X1")
print("Training Accuracy:", train_accuracy)
print("Testing Accuracy:", test_accuracy)
# Step 2: Add the independent variable with the next highest correlation and create a new linear model
X = data[['X1', 'X2']] # Add the independent variable with the next highest correlation
# Repeat the train-test split and model fitting process
# Step 3: Repeat Step 2 for additional independent variables, evaluating accuracy each time
```
You can repeat Step 2 and Step 3 by adding more independent variables to `X` and fitting the linear regression model. Remember to perform the train-test split for each model and set `random_state` to 0 to ensure consistent results.