Consider a classification problem where the input space consists of 10 binary features (X₁, X₂, ..., X₁₀) and the output space consists of two classes (0 and 1). A dataset of 100 training examples is given, with the following feature values and corresponding class labels:Example 1: (0, 1, 1, 0, 1, 0, 1, 0, 0, 1) → Class: 0Example 2: (1, 0, 0, 1, 1, 1, 0, 1, 0, 1) → Class: 1Example 3: (1, 1, 1, 0, 0, 1, 0, 0, 1, 0) → Class: 1Example 100: (0, 0, 1, 0, 1, 0, 1, 1, 0, 1) → Class: 0You are tasked with building a supervised learning model using logistic regression. Each feature Xi is binary and can take values 0 or 1. You decide to use one-hot encoding to represent the input features. After applying one-hot encoding, how many features will the dataset have for training the logistic regression model?a)19b)20c)10d)18

Consider a binary classification problem with two classes, A and B with prior probability P(A)=0.6𝑃(𝐴)=0.6 and P(B)=0.4𝑃(𝐵)=0.4 .Let X be a single binary feature that can take values 00 or 11. Given: P(X=1|A)=0.8𝑃(𝑋=1|𝐴)=0.8 and P(X=0|B)=0.7𝑃(𝑋=0|𝐵)=0.7. Determine which class the classifier will classify when X=1𝑋=1.

ppose we have a dataset of eight 3-bit binary vectors and their corresponding labels. Classify a new data point, 010,using a k-nearest neighbor classifier with k=3 and Hamming distance.Data 000 001 010 011 100 101 110 111 111 111point A A B B C C D D D D

A classifier-(1 Point)Inputs a vector of continuous values and outputs a single discrete valueInputs a vector of discrete values and outputs a single discrete valueBoth the aboveNone

Classifier Send Feedback Problem Statement: You are given a numerical input n. Write a function "classifier" that takes n as input and uses conditional statements to determine the appropriate category label based on the given input. Input: The function takes one integer as input n (int): The numerical input. Output: The function returns the category label. The category labels are as follows: 0 corresponds to "cat" 1 corresponds to "dog" Any other value corresponds to "human" Sample Input 1: classifier(0) Sample Return 1: cat Sample Input 2: classifier(1) Sample Return 2: dog Sample Input: classifier(5) Sample Return: human

k-NN: This problem derives a performance bound on the 1-NN classifier. Consider the 1-NN binaryclassifier on a two-dimensional space. For an input x and its nearest neighbor xn in the trainingdata, the conditional probabilities of belonging to classes c1 and c2 are as follows:◦ p(c1|x) : probability that x belongs to c1◦ p(c2|x) : probability that x belongs to c2◦ p(c1|xn) : probability that xn belongs to c1◦ p(c2|xn) : probability that xn belongs to c2Knowing that p(c1|x) + p(c2|x) = 1 and p(c1|xn) + p(c2|xn) = 1,(a) Express the conditional probability of misclassification error, e, for x, P (e|x, xn), in terms ofthe probabilities provided above.