Data Science

Machine Learning Interview Questions

1) What is the difference between Data Mining and Machine Learning?

Data Mining is a process focused on discovering hidden patterns and relationships in data. Machine Learning, on the other hand, is a branch of artificial intelligence that learns from data and builds models to make predictions or decisions. Data mining is more concerned with exploratory analysis, while machine learning focuses on generalizing results.

2) What is the difference between "Type 1" and "Type 2" errors?

Type 1 Error (False Positive): When the test indicates a positive result while the actual condition is negative. (False alarm)

Type 2 Error (False Negative): When the test indicates a negative result while the actual condition is positive. (Ignoring the true condition)

3) What should we know about the correlation between "True Positive Rate" and "Recall"?

True Positive Rate (TPR) and Recall essentially refer to the same concept and are calculated as the ratio of true positives to all actual positives. This indicates how well the model identifies true positives.

4) What is Backpropagation?

Backpropagation is an algorithm used to optimize weights in artificial neural networks. The output error is propagated back through each layer of the network, and the weights are updated accordingly.

5) What role do Variance and Bias play in Machine Learning?

Bias: It refers to the systematic error in the model's predictions. High bias can limit the model's capacity to learn.

Variance: It expresses the model's sensitivity to the training data. High variance can cause the model to overfit the training data and fail to generalize.

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