Introduction to Supervised Learning
Explore the basics of supervised learning to understand how models are trained using labeled data, differentiate from unsupervised methods, and learn to select appropriate evaluation metrics. This lesson helps you grasp classification and regression applications, understand training workflows, and evaluate model performance with commonly used metrics such as precision, recall, and ROC AUC.
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Supervised learning is a core topic in machine learning interviews. In this lesson, we’ll practice explaining key concepts clearly, compare supervised and unsupervised approaches, and learn how to choose the right evaluation metric for your model’s goals. Let’s get started.
Supervised learning basics
Can you explain the concept of supervised learning in machine learning? How does it differ from unsupervised learning? Could you provide some examples of applications that you’ve used supervised learning for, and what process you used to train a supervised learning model?
This question is frequently asked in almost every data science or ML screening across industries.
Sample answer
Supervised learning is a type of machine learning where the model is trained using labeled data. In this approach, each training example is associated with a label or output that the model aims to predict. The goal is to learn a mapping from input features to the target output.
Differences from unsupervised learning:
Supervised learning uses labeled data (input-output pairs) for training, while unsupervised learning uses unlabeled data.
Supervised learning aims to make predictions or classify new data points, whereas unsupervised learning focuses on finding hidden patterns or intrinsic structures in the data. Common types of unsupervised learning include clustering (grouping similar data points) and dimensionality reduction (reducing the number of variables under consideration):
Clustering: This technique groups similar data points together. Imagine you have a dataset of ...