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

Support Vector Machine is-(1 Point)a discriminative classifiera lazy learning classifiera probabilistic classifierNone

Decision trees where the target variable can take continuous values (typically real numbers) are calledSelect one:a. Regression treesb. Classification trees

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

Decision trees can handle2 pointshigh dimensional dataLow dimensional dataMedium dimensional dataNone of these

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