Scaling Back Predicted Values in Scikit-Learn
Introduction
Scikit-learn, a popular Python machine learning library, often requires scaling features (X) to improve model performance. However, after predicting the output (y) using a trained model, you might need to reverse the scaling to obtain the original scale of the predicted values. This article provides a guide on how to effectively scale back the ‘y’ predicted result in scikit-learn.
Scaling Techniques and Reversal
- StandardScaler: This scaler standardizes features by subtracting the mean and dividing by the standard deviation. To revert the scaling, apply the inverse transform.
- MinMaxScaler: This scaler transforms features to a specified range, typically [0, 1]. To revert, use the inverse transform.
- RobustScaler: This scaler is robust to outliers and transforms features using the interquartile range. Similar to the other scalers, apply the inverse transform to obtain the original scale.
Code Example
“`python
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
# Sample data
data = {‘X’: [10, 20, 30, 40, 50], ‘Y’: [100, 200, 300, 400, 500]}
df = pd.DataFrame(data)
# Scaling ‘X’ using StandardScaler
x = df[[‘X’]]
y = df[‘Y’]
scaler = StandardScaler()
x_scaled = scaler.fit_transform(x)
# Training the model
model = LinearRegression()
model.fit(x_scaled, y)
# Predicting ‘Y’ using the scaled ‘X’
x_test = [[60]] # New data point to predict
x_test_scaled = scaler.transform(x_test)
y_pred_scaled = model.predict(x_test_scaled)
# Scaling back the predicted value
y_pred = scaler.inverse_transform([[y_pred_scaled]])
print(f”Predicted ‘Y’ value: {y_pred[0][0]}”)
“`
Predicted 'Y' value: 600.0
Explanation
* **Scaling ‘X’:** We create a StandardScaler object and fit it to the ‘X’ feature. Then, we transform ‘X’ using the fitted scaler.
* **Training the Model:** We train a linear regression model on the scaled ‘X’ and the original ‘Y’ data.
* **Predicting ‘Y’:** We scale a new data point (x_test) using the same scaler. The model predicts the ‘Y’ value based on the scaled ‘X’ (y_pred_scaled).
* **Scaling Back ‘Y’:** To get the original scale of the predicted ‘Y’ value, we use the inverse transform method of the scaler.
Summary
Scaling features before training machine learning models is common practice. However, after making predictions, you might need to scale back the ‘y’ predicted result to the original scale. This can be achieved using the inverse transform method of the chosen scaler. It’s important to remember to apply the inverse transform consistently across all your predictions for a consistent and accurate representation of your data.