Supervised learning requires training labeled data. For example, in order to do classification (a supervised learning task), you’ll need to first label the data you’ll use to train the model to classify data into your labeled groups. Unsupervised learning, in contrast, does not require labeling data explicitly.
When asked about your favorite algorithm in a machine learning interview, it’s essential to choose an algorithm that you are genuinely comfortable with and can explain concisely. Here’s an example response for the k-nearest neighbors (KNN) algorithm:
“My favorite algorithm is k-nearest neighbors, or KNN. It’s a simple yet powerful supervised learning algorithm used for classification and regression tasks. In less than a minute, here’s how it works: Given a dataset with labeled examples, KNN classifies new data points by finding the ‘k’ nearest neighbors based on a chosen distance metric, typically Euclidean distance. For classification, the majority class among the k neighbors determines the class of the new data point. For regression, KNN computes the average or weighted average of the target values of the k neighbors to predict the target value for the new data point. It’s intuitive, easy to implement, and doesn’t require training, making it suitable for quick prototyping or baseline models.”
In the context of a machine learning interview question, the correct answer would be:
Supervised machine learning involves training a model on a labeled dataset, where each input sample is associated with a corresponding target or output label. The goal is to learn a mapping from input features to the output labels, based on the provided examples. During training, the model is guided by the supervision provided by the labeled data, aiming to minimize the discrepancy between its predictions and the actual labels. Examples of supervised learning tasks include classification (where the output is categorical) and regression (where the output is continuous).
Unsupervised machine learning, on the other hand, deals with datasets where the target labels are not provided. The objective is to find hidden structure or patterns within the data without explicit guidance. In unsupervised learning, the algorithm explores the data and identifies similarities, differences, or other relationships among the input samples. Common tasks in unsupervised learning include clustering (grouping similar data points together) and dimensionality reduction (reducing the number of input features while preserving essential information).
In summary, the main difference between supervised and unsupervised machine learning lies in the presence or absence of labeled data during the training phase, with supervised learning being guided by labeled examples and unsupervised learning operating on unlabeled data to discover patterns or structures autonomously.