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.

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

Quiz Time

Test your knowledge of Scikit Learn methods used in ML pipelines, their distinct functionalities, and when to apply each for optimal data preprocessing and model training.

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:

**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.-
**Feature Engineering**is the process of extracting features from raw data with some domain knowledge. **Feature Selection**is where we select those features from the dataframe that will give a high impact on the estimator.**Model creation**in this, we create a machine learning model using suitable algorithms, e.g., regressor or classifier.-
**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)**

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 **Fâ€™ **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 Fâ€™ itself So, in this condition, we do three different operations:

**fit()****transform()****fit_transform()**

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

Method | Purpose | Syntax | Example |
---|---|---|---|

fit() | Learn and estimate the parameters of the transformation | `estimator.fit(X)` | `estimator.fit(train_data)` |

transform() | Apply the learned transformation to new data | `transformed_data = estimator.transform(X)` | `transformed_data = estimator.transform(test_data)` |

fit_transform() | Learn the parameters and apply the transformation to new data | `transformed_data = estimator.fit_transform(X)` | `transformed_data = estimator.fit_transform(data)` |

**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)`

).

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 (Î¼)** and **standard deviation (Ïƒ)** 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.

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.

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(**Î¼**)and standard deviation(**Ïƒ**) 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.

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.

- 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().

A. Yes, transform() method can be used without using fit() method in scikit-learn. This is useful when we want to transform new data using the same scaling or encoding applied to the training data.

A. The fit_transform() method is used to fit the data into a model and transform it into a form that is more suitable for the model in a single step. This saves us the time and effort of calling both fit() and transform() separately.

A. The main limitation of these methods is that they may not work well with certain types of data, such as data with null values or outliers, and we might need to perform additional preprocessing steps.

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Become a full stack data scientist
##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

Understanding Cost Function
Understanding Gradient Descent
Math Behind Gradient Descent
Assumptions of Linear Regression
Implement Linear Regression from Scratch
Train Linear Regression in Python
Implementing Linear Regression in R
Diagnosing Residual Plots in Linear Regression Models
Generalized Linear Models
Introduction to Logistic Regression
Odds Ratio
Implementing Logistic Regression from Scratch
Introduction to Scikit-learn in Python
Train Logistic Regression in python
Multiclass using Logistic Regression
How to use Multinomial and Ordinal Logistic Regression in R ?
Challenges with Linear Regression
Introduction to Regularisation
Implementing Regularisation
Ridge Regression
Lasso Regression

Introduction to Stacking
Implementing Stacking
Variants of Stacking
Implementing Variants of Stacking
Introduction to Blending
Bootstrap Sampling
Introduction to Random Sampling
Hyper-parameters of Random Forest
Implementing Random Forest
Out-of-Bag (OOB) Score in the Random Forest
IPL Team Win Prediction Project Using Machine Learning
Introduction to Boosting
Gradient Boosting Algorithm
Math behind GBM
Implementing GBM in python
Regularized Greedy Forests
Extreme Gradient Boosting
Implementing XGBM in python
Tuning Hyperparameters of XGBoost in Python
Implement XGBM in R/H2O
Adaptive Boosting
Implementing Adaptive Boosing
LightGBM
Implementing LightGBM in Python
Catboost
Implementing Catboost in Python

Introduction to Clustering
Applications of Clustering
Evaluation Metrics for Clustering
Understanding K-Means
Implementation of K-Means in Python
Implementation of K-Means in R
Choosing Right Value for K
Profiling Market Segments using K-Means Clustering
Hierarchical Clustering
Implementation of Hierarchial Clustering
DBSCAN
Defining Similarity between clusters
Build Better and Accurate Clusters with Gaussian Mixture Models

Introduction to Machine Learning Interpretability
Framework and Interpretable Models
model Agnostic Methods for Interpretability
Implementing Interpretable Model
Understanding SHAP
Out-of-Core ML
Introduction to Interpretable Machine Learning Models
Model Agnostic Methods for Interpretability
Game Theory & Shapley Values

Deploying Machine Learning Model using Streamlit
Deploying ML Models in Docker
Deploy Using Streamlit
Deploy on Heroku
Deploy Using Netlify
Introduction to Amazon Sagemaker
Setting up Amazon SageMaker
Using SageMaker Endpoint to Generate Inference
Deploy on Microsoft Azure Cloud
Introduction to Flask for Model
Deploying ML model using Flask

I find myself wishing that you had provided the output for Fit= stand.fit(xtrain) to compare against the transform output. It seems like only a partial example, but you make a good case that fit_transform() is the same as the two other functions together.