$-$ What is $$overfitting$?$ what is its drawback? What are the approaches to take to avoid $$overfitting$?$

$-$ Aside from the Gini index and Entropy, explain two other parameters to measure purity $($best split$)$ in a decision tree.

$-$ What do the x$-$axis and y$-$axis represent in an ROC curve? What does the area under the curve represent? Draw an example ROC curve for a classifier that performs better than a random guess. Describe an application where false negatives might matter more than the accuracy of the classifier.

$-$ In a Neural Networks model, should we prefer a large hidden layer over a small one? Explain the drawbacks and benefits of each option.

$-$ Explain three combination functions that can be used in the K$-$Nearest Neighbor algorithm to make the final decision. Mention examples where you may recommend using these combination functions.

$-$ In the following neural networks, the input range is between $1$ and $10,$ and its current value is $5.$ The transfer function for the nodes in the hidden layer is Hyperbolic Tangent, and for the output node is a Logistic function. Calculate the output x$.$ Remember to normalize the input between $[0,1].$ Also, remember to scale back the output value x to its actual value $($i$.$e$.$ de$-$normalize the output value x$).$

$-$ Consider the training data set shown in the following Table. We want to classify the new customer C#$7$ with the attributes $<$ P$,$ $$3$k$,$ $$10$k $>$ using the K$-$Nearest Neighbor algorithm with K$=3$ and a simple majority voting. To calculate distances between every two records, consider the $3$ attributes $$Education$,$Saving$,$ and $$Assets$.$ For the $$Education$$ attribute, consider the following distance function: