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posted on 14 Oct 2024
Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.
Supervised learning requires a target variable to predict, while unsupervised learning does not.
In supervised learning, the model learns from the labeled training data and makes predictions on new data. In unsupervised learning, the model finds patterns and relationships in the data without guidance.
Examples of sup...
Understanding outliers, missing values, overfitting, and underfitting in data science.
Outliers are data points that significantly differ from other data points in a dataset.
Missing values are data points that are not present in a dataset.
Overfitting occurs when a model learns noise in the training data rather than the underlying pattern.
Underfitting occurs when a model is too simple to capture the underlying pattern in...
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