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Supervised learning is a type of machine learning where an algorithm learns from labeled training data to make predictions or decisions without human intervention. There are several types of predictions that can be made using supervised learning, depending on the nature of the problem and the type of output variable. There are two main types of supervised learning:

1. Classification: In classification tasks, the goal is to predict a categorical label or class for a given input. Examples include spam email detection (classifying emails as spam or not), image classification (identifying objects in images), and sentiment analysis (classifying text as positive, negative, or neutral).

2. Binary Classification: This is a specific type of classification where there are only two possible classes or labels. Examples include fraud detection (fraudulent or not), disease diagnosis (sick or healthy), and customer churn prediction (churn or not churn).

3. Multi-class Classification: In multi-class classification, there are more than two possible classes. Examples include hand-written digit recognition (classifying digits from 0 to 9), language identification (identifying the language of a text), and species classification (classifying animals into different species).

4. Regression: Regression tasks involve predicting a continuous numeric value or a quantity. Examples include predicting house prices, stock prices, temperature forecasts, and age prediction.

5. Time Series Forecasting: Time series data involves observations recorded at different time points. In time series forecasting, the goal is to predict future values of a time-dependent variable. Examples include stock price forecasting, weather forecasting, and demand forecasting for inventory management.

6. Anomaly Detection: Anomaly detection aims to identify rare or abnormal instances in a dataset. It is often used for fraud detection, network intrusion detection, and equipment failure prediction.

7. Ranking: Ranking problems involve predicting the order or preference of items. Examples include search engine ranking (ranking web pages by relevance), recommendation systems (ranking products or content for users), and sports team ranking.

8. Object Detection: Object detection combines classification and localization to identify and locate objects within images or video frames. It's commonly used in autonomous vehicles, surveillance, and facial recognition systems.

9. Semantic Segmentation: Semantic segmentation involves classifying each pixel in an image into a specific category. This is used in tasks like image segmentation for autonomous driving and medical image analysis.

10. Named Entity Recognition (NER): NER is a specific type of sequence labeling where the goal is to identify and classify named entities (such as names of people, organizations, and locations) in text data.

Table 3916. Types of predictions with Supervised Learning.

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Difference between regression and classification. Code:
         
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