Machine learning is a powerful field that involves training models to identify patterns in large datasets and make accurate predictions based on those patterns. Two key terms used in machine learning are fit and transform. While these terms may seem similar, they refer to distinct processes that are critical to the success of any machine-learning project.
In this thread, we’ll take a closer look at the differences between fit and transform and explain how they are used in machine learning. Whether you’re a seasoned data scientist or just starting out, understanding these fundamental concepts is essential for building accurate and effective machine learning models.
Understanding the concept of "Fit"
Fit refers to the process in machine learning where you are training a model on a dataset. When you fit a model, you are basically adjusting the model parameters and making the model learn the data you provided to find the best possible fit for the data or in other words, to find the best possible model parameters.
This process of fitting the model to the data is typically done in the training stage of a machine learning project using an algorithm that minimizes some measure of error between the predicted and actual values.
Understanding the concept of "Transform"
Transform, on the other hand, refers to the process in machine learning where you are applying a function to a dataset to transform or prepare it in some way. Transforming the dataset is also an important step in machine learning projects and can involve anything from scaling or normalizing the numerical data to encoding categorical variables.
In the context of machine learning, transform is often used in conjunction with fit. Once a model has been fit to a training dataset, it can be used to make predictions on new data. However, before making these predictions, the new data must be transformed in the same way as the training data which is done using transform. This ensures that the model is making predictions on data that is in the same format as the data it was trained on.
An example to clear the concepts further
Let’s consider an example now which will also show you the steps where these concepts are used and how they are used. Before going to the step of fitting and transforming, we first load a dataset and you can view its output by running the code below.
Now the process of the code below goes like this:
- After importing the libraries, the dataset is split into training and testing sets. Both sets have different arrays of the feature inputs and the target output.
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StandardScalartransformer is created which is used to scale values such that they have mean 0 and standard deviation 1. - The training set features (
X_train) are transformed usingfit_transformcausing thescalarobject to first learn the patterns in the numerical values and then transform/scale them. - A Logistic Regression model is created which is then trained on the transformed features and their outputs by using the
fitfunction which is responsible for training the model on the data provided. - The testing set features (
X_test) are transformed usingtransformcausing the new unseen data to be transformed and scaled in the same manner as the training features were. - Finally, the transformed testing features are used by the model to predict the output/target and then accuracy is calculated by comparing predicted values and actual values.