Difference Between fit(), transform(), and fit_transform() Methods in Scikit-Learn

Scikit-Learn is a powerful machine learning library that provides various methods for data preprocessing and model training. In this article, we will explore the distinctions between three commonly used methods: fit(), transform(), and fit_transform() sklearn. Understanding these methods is crucial for effectively using Scikit-Learn in machine learning projects. We will delve into the purpose and functionality of each method, as well as when and how to use them. By the end of this article, you will clearly understand how to apply these methods in Scikit-Learn to enhance your data analysis and model building. In this article, you will get to learn about the fit transform and difference between fit vs fit_transform, transform and fit transform these are the some differences we have telling in this article.

This article was published as a part of the Data Science Blogathon.

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Data Science Project Life Cycle

Before we start exploring the fit transform, and fit_transform functions in Python, let’s consider the life cycle of any data science project. This will give us a better idea of the steps involved in developing any data science project and the importance and usage of these functions. Let’s discuss these steps in points:

1. Exploratory Data Analysis (EDA) is used to analyze the datasets using pandas, numpy, matplotlib, etc., and dealing with missing values. By doing EDA, we summarize their main importance.
2.  Feature Engineering is the process of extracting features from raw data with some domain knowledge.
3. Feature Selection is where we select those features from the dataframe that will give a high impact on the estimator.
4.  Model creation in this, we create a machine learning model using suitable algorithms, e.g., regressor or classifier.
5.  Deployment where we deploy our ML model on the web.

If we consider the first 3 steps, then it will probably be more towards Data Preprocessing, and Model Creation is more towards Model Training. So these are the two most important steps whenever we want to deploy any machine learning application.

Check out – Introduction to Life Cycle of Data Science projects (Beginner Friendly)

Transformer In Sklearn

Scikit-learn has an object, usually, something called a Transformer. The use of a transformer is that it will be performing data preprocessing and feature transformation, but in the case of model training, we have learning algorithms like linear regression, logistic regression, knn, etc., if we talk about the examples of Transformer-like StandardScaler, which helps us to do feature transformation where it converts the feature with mean =0 and standard deviation =1, PCA, Imputer, MinMaxScaler, etc. then all these particular techniques have seen that we are doing some preprocessing on the input data will change the format of training dataset, and that data will be used for model training.

Suppose we take f1, f2, f3, and f4 features where f1, f2, and f3 are independent features, and f4 is our dependent feature. We apply a standardization process in which it takes a feature F and converts it into F’ by applying a formula of standardization. If you notice, at this stage, we take one input feature F and convert it into another input feature F’ itself So, in this condition, we do three different operations:

1. fit()
2. transform()
3. fit_transform()

Now, we will discuss how the following operations are different from each other.

Difference Between fit and fit_transform Sklearn

Note: In the syntax, estimator refers to the specific estimator or transformer object from Scikit-Learn that is being used. X represents the input data.

Example: Suppose we have a dataset train_data for training and test_data for testing. We can use fit() to learn the parameters from the training data (estimator.fit(train_data)) and then use transform() to apply the learned transformation to the test data (transformed_data = estimator.transform(test_data)). Alternatively, we can use fit_transform() to perform both steps in one (transformed_data = estimator.fit_transform(data)).

fit()

In the fit() method, where we use the required formula and perform the calculation on the feature values of input data and fit this calculation to the transformer. For applying the fit() method (fit transform in python), we have to use fit() in frontof the transformer object.

Suppose we initialize the StandardScaler object O and we do .fit(). It takes the feature F and computes the mean (μ) and standard deviation (σ) of feature F. That is what happens in the fit method.

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# split training and testing data
xtrain,xtest,ytrain,ytest= train_test_split(
                                            x,y,
                                            test_size=0.3,
                                            random_state=42
                                           )
# creating object 
stand= StandardScaler()
# fit data
Fit= stand.fit(xtrain)

First, we have to split the dataset into training and testing subsets, and after that, we apply a transformer to that data.

In the next step, we basically perform a transform because it is the second operation on the transformer.

transform()

For changing the data, we probably do transform in the transform() method, where we apply the calculations that we have calculated in fit() to every data point in feature F. We have to use .transform() in front of a fit object because we transform the fit calculations.

The above example when we create an object of the fit method. We then put it in front of the .transform, and the transform method uses those calculations to transform the scale of the data points, and the output will we get is always in the form of a sparse matrix or array.

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# split training and testing data
xtrain,xtest,ytrain,ytest= train_test_split(
                                            x,y,
                                            test_size=0.3,
                                            random_state=42
                                           )
# creating object
stand= StandardScaler()
# fit data
Fit= stand.fit(xtrain)
# transform data
x_scaled = Fit.transform(xtrain)

As you can see that the output of the transform is in the form of an array in which data points vary from 0 to 1.

Note: It will only perform when we want to do some kind of transformation on the input data.

fit_transform() Sklearn

The fit_transform() Sklearn method is basically the combination of the fit method and the transform method. This method simultaneously performs fit and transform operations on the input data and converts the data points.Using fit and transform separately when we need them both decreases the efficiency of the model. Instead, fit_transform() is used to get both works done.

Suppose we create the StandarScaler object, and then we perform .fit_transform(). It will calculate the mean(μ)and standard deviation(σ) of the feature F at a time it will transform the data points of the feature F.

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# split training and testing data
xtrain,xtest,ytrain,ytest= train_test_split(
                                            x,y,
                                            test_size=0.3,
                                            random_state=42
                                           )
stand= StandardScaler()
Fit_Transform = stand.fit_transform(xtrain)
Fit_Transform

This method output is the same as the output we obtain after applying the separate fit() and transform() methods.

Conclusion

In conclusion, the scikit-learn library provides us with three important methods, namely fit(), transform(), and fit_transform() Sklearn, that are used widely in machine learning. The fit() method helps in fitting the data into a model, transform() method helps in transforming the data into a form that is more suitable for the model. Fit_transform() method, on the other hand, combines the functionalities of both fit() and transform() methods in one step. Understanding the differences between these methods is very important to perform effective data preprocessing and feature engineering.

Hope you like the article and get understnding about the fit transform and also you Now know about the difference between fit_transform and transform with that major difference between fit and fit_transform.

Key Takeaways

• The fit() method helps in fitting the training dataset into an estimator (ML algorithms).
• The transform() helps in transforming the data into a more suitable form for the model.
• The fit_transform() method combines the functionalities of both fit() and transform().

Mayur