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# Machine learning quiz questions TRUE or FALSE with answers, important machine learning interview questions for data science, Top 3 machine learning question set

## Machine Learning TRUE / FALSE Questions - SET 03

### 1. MAP estimates are equivalent to the ML estimates when the prior used in the MAP is a uniform prior over the parameter space.

(a) TRUE                                                   (b) FALSE

### The only difference between MLE and MAP is, in MAP the prior probability P(θ) is included. In simpler terms, the likelihood is weighted with prior probability to become MAP. We can say MLE is a special case of MAP when prior follows a uniform distribution.

Uniform prior is about assigning equal weights everywhere like a constant.
Hence, MAP estimates are equivalent to the MLE when the prior used in the MAP is a uniform prior over the parameter space.
[Refer for more: MLE Vs MAP]

### 2. Because decision trees learn to classify discrete-valued outputs instead of real-valued functions it is impossible for them to overfit.

(a) TRUE                                                   (b) FALSE

Overfitting is possible in decision trees. If a decision tree is fully grown, it may lose some generalization capability. This is a phenomenon known as overfitting.
Not just a decision tree, (almost) every ML algorithm is prone to overfitting.

### What is overfitting?

Over-fitting is the phenomenon in which the learning system tightly fits the given training data so much that it would be inaccurate in predicting the outcomes of the untrained data. One simple way to understand this is to compare the accuracy of your model w.r.t. to training set and test set. If there is a huge difference between them, then your model has achieved overfitting.

### 3. If P(A|B) = P(A) then P(A ∩ B) = P(A)P(B).

(a) TRUE                                                   (b) FALSE

 Answer: TRUE The joint probability of the random variables A and B together are actually separable into the product of their individual probabilities. This states that the probability of any outcome of A and any outcome of B occurring simultaneously is the product of those individual probabilities. P(A|B) = P(A) – this implies that A is independent of B. Conditional probability P(A|B) = P(A ∩ B)/P(B) Multiplication rule: P(A ∩ B) = P(A|B)P(B). If P(A|B) = P(A), then P(A ∩ B) = P(A)P(B).

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