What is feature scaling and how can you use it in machine learning?

Feature selection comes as the pre-processing of data, meaning you do it before starting any ML model. Once you know your variable which can impact your result (supervise learning), you do feature scaling. Think you have two variables, 'a' has values between 1 to 10, and variable (b) has 1 to 1000. here 'b' will impact your result much more than variable 'a', in other words even if 'a' has a very significant contribution to the final result, the machine will not understand. To avoid such a situation you do 'Scaling' which means you are putting everything under one scale, between -3 to 3 or -1 to1. But not necessary you must be scaling in all the cases, for example, ensemble method - where you don't need it, Can you tell why?

Feature scaling in machine learning normalizes numerical features to a standard range, allowing algorithms to interpret them uniformly. It's crucial as it enhances model performance and accuracy and resolves issues like overfitting and numerical instability. Methods like normalization, standardization, min-max, and robust scaling depend on the data's nature. With scaling, models might understand the varying degrees of features' magnitude and range, impacting the learning process significantly. It is a vital preprocessing step, especially when dealing with real-world datasets with diverse features.

Feature scaling is a crucial preprocessing step in many machine-learning algorithms. It involves transforming the features to a common scale, which helps improve the convergence of gradient descent and ensures that the features contribute equally to the analysis. Two common methods for feature scaling are Min-Max Scaling (Normalization) and Standardization (Z-score normalization).

Feature scaling ensures that all features have the same contribution to the training process. It also prevents a specific feature from dominating the training process.

Suppose You're training a model to predict health risks. One feature is height (in centimeters) and another is weight (in kilograms). Without scaling, the model might be overly influenced by weight due to its larger values. Feature scaling, like standardization, puts both height and weight on the same scale, allowing the model to consider them equally for accurate predictions.

While building an apartment price predictor model, we employed a normalization process for all numerical attributes, including factors like the number of rooms and the size of each apartment. This data enhanced the model's learning capabilities. Normalizing these features, significantly improved the model's accuracy and overall performance.

Scaling features is generally not necessary when using Decision Trees. Decision tree algorithms, such as CART (Classification and Regression Trees) work by recursively splitting data based on feature thresholds. These splits are determined by comparing the values of individual features to find the best split that maximizes information gain or minimizes impurity or MSE, depending on the target variable and criteria used. Feature scaling generally doesn't impact these splits. As long as the relative ordering of values is preserved, the tree can make effective splits. Also, Decision Trees are often used for their interpretability, and scaling can distort the original feature values, making it harder to interpret the tree's decisions.

Here are some additional feature scaling methods: Robust Scaler MaxAbs Scaler Unit Vector Scaling (Normalization to the unit norm) Decimal Scaling Mean Normalization Log Transformation Power Transformation Quantile Transformer Rank Transformation Box-Cox Transformation Yeo-Johnson Transformation