Sure, let's go through each of the questions with brief answers:
What is a training dataset in Supervised Learning?
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A training dataset is a collection of data used to train a supervised
learning model. It includes input data along with corresponding output
labels.
How do bias and variance impact Supervised Learning models?
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Bias is the error due to overly simplistic assumptions, causing underfitting.
Variance is the error due to too complex assumptions, leading to
overfitting. A good model balances both.
What is the purpose of a test dataset in Supervised Learning?
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A test dataset is used to evaluate the performance of a model on new,
unseen data. It helps to assess the model's generalization capabilities.
Can you explain the concept of a decision tree in Supervised Learning?
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A decision tree is a flowchart-like structure where each internal node
represents a test on an attribute, each branch represents the outcome of
the test, and each leaf node represents a class label.
What is the role of a loss function in Supervised Learning?
●
A loss function measures how well a model's predictions match the actual
labels. The goal of training is to minimize this loss.
