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DA📍Supervised Machine Learning Algorithms (Episode 75 of 100): DM to Download the free PDF👇
1. Supervised machine learning is a type of machine learning that learns the relationship between input and output. The inputs are known as features or ‘X variables’ and output is generally referred to as the target or ‘y variable’. The type of data which contains both the features and the target is known as labeled data.
2. What is Supervised Machine Learning?
Supervised machine learning learns patterns and relationships between input and output data. It is defined by its use of labeled data. A labeled data is a dataset that contains a lot of examples of Features and Target. Supervised learning uses algorithms that learn the relationship of Features and Target from the dataset. This process is referred to as Training or Fitting.
3. There are two types of supervised learning algorithms:
✅ Classification
✅ Regression
4. Regression:
Regression is a type of supervised machine learning where algorithms learn from the data to predict continuous values such as sales, salary, weight, or temperature.
5. Classification:
Classification is a type of supervised machine learning where algorithms learn from the data to predict an outcome or event in the future.
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