Learn the difference between feature selection, feature engineering, and model training.
Table of Contents
Question
Let’s suppose you are working on an Al application that should predict the weather. From the dataset you have, you want to pick temperature and pressure to train the model. Which machine learning task enables you to do that?
A. Feature selection
B. Model training
C. Feature engineering
Answer
A. Feature selection
Explanation
Feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model.
The correct answer to the question is A. Feature selection. Feature selection is the process of choosing a subset of relevant features from the original dataset that can improve the performance and efficiency of the machine learning model. In this case, temperature and pressure are two features that are likely to have a strong correlation with the weather, so selecting them can help the model make better predictions.
Feature engineering, on the other hand, is the process of creating new features from the existing ones by applying some transformations or calculations. For example, one could create a new feature called humidity by dividing the water vapor pressure by the atmospheric pressure. Feature engineering can also improve the model’s performance, but it is not the same as feature selection.
Model training is the process of fitting the machine learning model to the data by adjusting its parameters using a learning algorithm. Model training requires a set of features and labels (the target variable) as inputs, and it outputs a trained model that can make predictions on new data. Model training is not the task of picking features from the dataset, but rather using them to train the model.
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