The fit() vs. fit_transform() methods in scikit-learn

In scikit-learn, the fit() and fit_transform() methods are commonly used in the context of data preprocessing and machine learning pipelinesThis refers to the entire data input and output process of a machine learning model., especially with transformers and feature extraction techniques.

The `fit()` method

The fit() method is typically used for training or fitting a transformer or a model to the data.
It computes and stores internal parameters or statistics based on the data provided during the fitting process. These parameters are necessary for subsequent transformations or predictions.
It is used when we want to learn from the data and extract important information, such as calculating mean and standard deviation for feature scaling or learning the vocabulary for text data transformationText data transformation here refers to extract the useful data from the raw data..
It returns the fitted transformer or model and does not directly modify the input data.

In the following example, we use the StandardScalerIt is used for standardizing features by removing the mean and scaling to unit variance. The idea behind standardization is to transform the data so that it has a mean of 0 and a standard deviation of 1. transformer to standardize the input data:

import numpy as np
from sklearn.preprocessing import StandardScaler
# Create a sample dataset with two features
data = np.array([[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]])
# Create a StandardScaler instance
scaler = StandardScaler()
# Using the fit() method to compute mean and standard deviation
scaler.fit(data)
# Now, we can access the mean and standard deviation if needed
mean = scaler.mean_
std_dev = scaler.scale_
# Using the fit_transform() method to standardize the data
data_scaled = scaler.fit_transform(data)
# The original data
print("Original data:")
print(data)
# The standardized data
print("Standardized data:")
print(data_scaled)
# Mean and standard deviation
print("Mean:", mean)
print("Standard Deviation:", std_dev)

In this example, we first create a sample data dataset with two features. We then create a StandardScaler instance called scaler. We use the fit() method to compute the mean and standard deviation of each feature in the dataset. We can access the computed mean and standard deviation using the scaler.mean_ and scaler.scale_, respectively, and these values can be necessary for subsequent transformations or predictions.

Next, we use the fit_transform() method to standardize the data in a single step and store it in data_scaled.

Finally, we print both the original data and the standardized data, as well as the mean and standard deviation. The standardized data will have a mean of approximately 0 and a standard deviation of approximately 1 for each feature, indicating that the data has been standardized.

fit() vs. fit_transform()

Aspect	The `fit()` Method	The `fit_transform()` Method
Purpose	Primarily focused on optimizing the learning phase without modifying the data directly. It aims to reduce computational overhead during parameter estimation.	Combines optimization with immediate transformation, potentially reducing the need for separate transformation steps.
Memory Efficiency	May require additional memory for intermediate result storage.	Can be more memory-efficient, especially with operations involving intermediate data.
Impact on Performance	Optimizing the `fit()` method can lead to faster training and learning of model parameters, potentially improving overall model performance.	The `fit_transform()` method can save computational time and simplify workflows by immediately applying transformations during preprocessing, which can improve pipeline efficiency.
Memory & Computational Trade-off	Lower memory usage, potentially faster as it solely trains the model.	Potentially more memory-efficient but could be slower because it combines training and transformation, especially for memory-intensive tasks