What are the differences between supervised and unsupervised learning?

Supervised Learning is like a mentor(supervisor) who provides tips and guidance, corrects mistakes, and helps you improve. This algorithm learns from the labeled data(predictors and response variable), making predictions and adjusting its parameters to minimize the loss function.

In supervised learning, the algorithm is trained on a dataset consisting of input-output pairs, where the inputs are features or attributes, and the outputs are corresponding labels or target variables. The goal is to learn a mapping function from the input to the output, allowing the model to make predictions on unseen data. During training, the algorithm adjusts its parameters to minimize the difference between its predictions and the actual labels, typically measured by a predefined loss function.

Supervised learning meaning your model is trained with a pair of input data and desired output called labels. The training data is processed, building a function that maps new data on expected output values. Linear regression, logistic regression, decision trees, are some of the examples of supervised learning algorithms

Supervised Learning as per me: When we write set of instructions which will process the labelled data and predict the outcome based on the data it is trained with. Here Labelled data means the raw data with a definite name assigned for it to bring more meaning.

Supervised learning uses labeled data to train models to make predictions or classifications, while unsupervised learning works with unlabeled data to find hidden patterns or structures. Supervised learning requires target outputs for training, whereas unsupervised learning explores data without predefined outcomes.

Suppose you are exploring a new city without a tour guide or a map. You just rely on the patterns or structures of the landmarks, streets, and buildings. Similarly in unsupervised learning, you don't have labels. You just aim to discover patterns in the data. This is done by clustering, anomaly detection, pattern recognition, etc. Dimensionality reduction techniques like Principal Component Analysis(PCA) are also a part of unsupervised learning that reduces redundancies in the data and helps keep the important stuff.

Unsupervised Learning as per me: When we write set of instructions which will process the unlabelled data and predict the outcome based on the data pattern. Here unlabelled data means the raw data without a definite tag to it and it is fed to models to discover the correlation.

Unlike supervised learning, there are no predefined target variables to predict. Instead, the algorithm explores the inherent structure of the data to uncover hidden patterns or relationships. Common techniques in unsupervised learning include clustering, where data points are grouped into clusters based on similarity, and dimensionality reduction, which aims to reduce the number of features while preserving essential information. By discovering meaningful insights and patterns from unlabeled data, unsupervised learning techniques offer valuable insights into the underlying structure of the data.

As name suggests, unsupervised learning meaning learn own your own. In unsupervised learning, a model is trained on a dataset but it doesn't have predefined labels. Unsupervised learning algorithm based models discover patterns and insights without any explicit guidance or instruction. K-means and hierarchical clustering are some of the unsupervised learning algorithms.

Unsupervised learning tasks are primarily intended to map the data features into another plane and encapsulate the learning into a useful model. Later use this model to multiple targeted machine learning tasks. Many of the modern world data science projects use this technique. For example, we use the word2vec or bert kind of models to generate the vector from our dataset later use that to feed into a supervised algorithm for target classification.

In supervised learning, the algorithm is trained on labeled data, where each data point is associated with a corresponding target variable or label. This labeled data provides explicit feedback to the algorithm, guiding it towards learning the relationship between input features and output labels. On the other hand, unsupervised learning operates on unlabeled data, where there are no predefined target variables. Instead, the algorithm explores the inherent structure and patterns within the data itself, without the need for explicit supervision. This fundamental difference in learning data dictates the objectives and methodologies of each learning approach.

In my opinion, the biggest difference between supervised and unsupervised learning algorithms is the type of the data. Supervised learning required a pair of data which include both features and output called labels i.e. models are trained on historical data having predefined outputs. While unsupervised learning models are trained on data with input features only, without labels.

In supervised learning, algorithms are primarily focused on regression and classification tasks. Regression algorithms predict continuous target variables, based on input features. Classification algorithms, on the other hand, assign data points to predefined categories or classes. Common supervised learning algorithms include linear regression, decision trees, support vector machines, and neural networks. In contrast, unsupervised learning algorithms are more diverse and versatile. They include clustering algorithms, and dimensionality reduction techniques, which aim to reduce the complexity of the data by representing it in a lower-dimensional space.

1. Supervised Learning uses labelled training data to predict or classify new data based on patterns learnt. Example – Algorithms that uses data set of house features and prices to predict the price of other houses. 2. Unsupervised Learning allows the machine to learn from available data. Without being given specific instructions, these algorithms can find patterns and organize data on their own. Example – Customer data to segment customers

In my opinion, the biggest difference between supervised and unsupervised learning is the data used. In the scenarios where we have pair of historical data or input data called features and output data called labels, supervised learning is used such as stock prices prediction or diseases diagnosis from symptoms. While Unsupervised learning algorithms are used in the scenarios where we don't have pre-defined outcome i.e. we want to explore the relation, patterns between different features

In supervised learning, where the goal is to predict a target variable, model performance is typically assessed by comparing the predicted values to the actual labels using evaluation metrics such as accuracy, precision, recall, F1-score, or mean squared error, depending on the specific task (classification or regression). In contrast, evaluating models in unsupervised learning is often more challenging since there are no explicit target variables to predict. Instead, evaluation focuses on assessing the quality of the discovered patterns or structures within the data.

In supervised learning, model evaluation involves assessing the performance of the model on labeled data, where the true outcomes are known. Common evaluation metrics include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC). In contrast, unsupervised learning lacks labeled data, making traditional evaluation metrics impractical. Instead, evaluation focuses on assessing the quality of the model's output based on its ability to uncover meaningful patterns or structures in the data. Techniques such as clustering validation indices (e.g., silhouette score, Davies–Bouldin index) or visualization methods (e.g., t-SNE, PCA) are commonly used.

Choosing the metric to evaluate a model has to be done at the design phase of the business problem definition. Why ? the challenge of model metrics is to be able to map it to specific business metrics. This way, it will be easy for DS teams and Business teams to discuss and build robust DS models