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- Discuss which of the following machine learning models you would choose to use in a given situation with the assistance of examples. There are two types of clustering methods: (a) K closest neighbour (c) Reversion to the past (d) increasing your knowledge basePresent a demonstration of Colab. Compare and contrast the models presented in the attached PDF file (Hands-On Unsupervised Learning Chapter 2.pdf) , with all the models presented in Book 1: Machine Learning Bookcamp: Logistic Regression, Random Forests, Gradient Boosting (XGBoost), Gradient Boosting (Light GBM). Which model in the attached PDF gave the best result?With the help of examples, discuss under what circumstances would you prefer using each of the following machine learning models. (a) K nearest neighbor (k-NN) (b) k-mean clustering (c) Regression (d) Deep learning
- Which of the following statements is true? Group of answer choices When clustering, we want to put two dissimilar data objects into the same cluster Clustering is among unsupervised learning models since it does not require a target variable Clustering is among unsupervised learning models since it requires a target variable When clustering, we want to put data objects into a pre-labeled target variable.TODO: Lienar Regression with least Mean Squares (LMS) Optimize the model through gradient descent. *Please complete the TODOs. * !pip install wget import osimport randomimport tracebackfrom pdb import set_traceimport sysimport numpy as npfrom abc import ABC, abstractmethodimport traceback from util.timer import Timerfrom util.data import split_data, feature_label_split, Standardizationfrom util.metrics import msefrom datasets.HousingDataset import HousingDataset class BaseModel(ABC): """ Super class for ITCS Machine Learning Class""" @abstractmethod def fit(self, X, y): pass @abstractmethod def predict(self, X): pass class LinearModel(BaseModel): """ Abstract class for a linear model Attributes ========== w ndarray weight vector/matrix """ def __init__(self): """ weight vector w is initialized as None """ self.w = None # check if the matrix is 2-dimensional. if…computer problem 1 can be found here:https://www.bartleby.com/questions-and-answers/blems-1.-design-a-monte-carlo-simulation-to-estimate-the-probability-of-a-random-walk-reaching-the-t/0892d4b2-194d-4f0d-869a-ae3b8e101eb9 DON'T LABLE THIS INCOMPLETE UNTIL YOU LOOK AT THAT LINK USE EITHER PYTHON OR MAPLE FOR THESE (or matlab if you know it)DO NOT SOLVE THESE BY HAND. If you solve these by hand I will thumbs down your answer.
- Select what you think is correct (multiple options are possible)? A) Logistic regression is a parametric classification algorithm and decision tree B) Logistic regression is based on a linear combination of parameters as is decision tree C) Logistic regression is based on a linear combination of parameters and a link function called sigmoid D) Decision trees tend to have high bias and low variance that random forests fix E) Decision trees use unsupervised learningProvide reasons for preferring one machine learning model over another. There are two main ways to classify things: K-nearest neighbor (a), looking back (c), and learning more (d) are all examples of this.?Question 47.Random forests are one of the most famous machine learning methods. They are easy to understand,easy to implement and reach good prediction performances even without a hyper-parameter tuning. Which of thefollowing statements on random forest are correct?a) The prediction of a classification forest is made by a majority vote of the trees’ predictions.b) The prediction of a regression forest is the median of the tree predictions.c) Each single tree in the forest uses only a part of the data available.d) The training time of a random forest scales linear with the number of trees used.
- Use python machine learning. Answer the following questions: 1. How does Random Forest work? Why is it better than a single decision tree? 2. Why is Random Forest better than a single decision tree? How does it decrease model error? How does it affect bias and virance? 3. What is Bagging?Using particular instances, describe why you would choose one machine learning model over another. There are two different clustering methods: Looking backward (A), using K-nearest neighbour (C), or learning more (D)?Can any decision tree be encoded using a set of classification rules? Yes No Which of the following data mining tasks is NOT a regression problem? Predicting tomorrow’s humidity Predicting the winner in a competition Predicting the price of a stock Predicting the vote counts of candidates in an election