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- Supervised: All data is labeled and the algorithms learn to predict the output from the input data.
- Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data.
Supervise ML Unsupervised ML
- Applied for labeled data . 1. Applied for unlabeled data.
- Can make predictions from past data. 2. Can be used to draw some hidden patterns from the predictor variables.
- ex:-Linear Regression,Random Forest. 3. Ex:- K-Means,correlation matrix,Hierarchical clustering.
- Uses Trained Data 4. Uses entire data Number of classes are known.
- Number of classes are known. 5. Number of classes are unknown.
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