9. What can be accomplished with a decision tree learning? Generate a new data set. Predict unknown feature values. Predict class attribute of the new data. Train data with unknown classes. 8. In terms of variables to model, what is a property of linear regression? Linear regression solves the same set of problems that can be solved by cluster analysis: as long as we have independent variables. linear regression can still be used to do some analyses Linear regression can only fit one dependent variable by one independent variable, There is a linear relationship between the dependent variable and independent variables. Values of dependent variables can never be real numbers.

9. What can be accomplished with a decision tree learning? Generate a new data set. Predict unknown feature values. Predict class attribute of the new data. Train data with unknown classes. 8. In terms of variables to model, what is a property of linear regression? Linear regression solves the same set of problems that can be solved by cluster analysis: as long as we have independent variables. linear regression can still be used to do some analyses Linear regression can only fit one dependent variable by one independent variable, There is a linear relationship between the dependent variable and independent variables. Values of dependent variables can never be real numbers.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Inputs Processing Output The accuracy in the output depends on the correct inputs. The model designed is supposed to be evaluated to determine the accuracy and it is supposed to function in a supervised way. Hence the data sets can affect the model and vice versa. Given P(D|M), P(M),and P(D'|M), solve the following a. Draw the probability tree for the main situation. b. Draw the reverse tree for the main situation. C. Using the main tree, prove all probabilities on the opposite tree. d. If P(D|M) = 0.3, P(M) = 0.1,and P(D'\M) = 0.65 find P(D) %3D
The accuracy in the output depends on the correct inputs. The model designed is supposedto be evaluated to determine the accuracy and it is supposed to function in a supervisedway. Hence the data sets can affect the model and vice versa.Given P(D|M) ,P(M) ,and P(D'|M), solve the followinga)Draw the probability tree for the main situationB)Draw the reverse tree for the main situation.C)Using the main tree, prove all probabilities on the opposite tree.D) If P(DIM) = 0.3,P(M) = 0.1 ,and P(D'IM) = 0.65 find P(D)
Question 48. Let us return to the Titanic data set. We now have learned several models and want to choose the best one. We used three different methods to validate these models: The training error rate (apparent error rate), the error rate on an external test set and the error rate estimated by a 10-fold cross validation. Training Error | Error on the test set | Cross Validation Error 0.18 Learner Decision Tree 0.22 0.21 Random Forest 0.01 0.10 0.12 1-Nearest-Neighbour 0.18 0.19 Which of the following statements are correct? a) 1-Nearest-Neighbour has a perfect training error and hence it should be used here. b) Random Forests outperforms both 1-Nearest-Neighbour and the Decision Tree in terms of prediction error. c) Not just in this case, but in general, Cross Validation is the better validation strategy and should always be preferred over the error on a single test set. d) Not just in this case, but in general, Decision Trees always perform worse than Random Forests.
Subject: Machine Learning Question Number 5 : What are Ensemble Algorithms? Write an algorithm for BOOSTING method. Assume that two individuals offer to sell you their predictive models M1 and M2. The confusion matrices produced by each model are as follows. What is the accuracy of each model? Assuming that precision is of paramount importance in your application, which of the two models would you buy? Why?
In the context of Machine Learning, what is Gradient Descent Optimization? • Your choice: correct- The iterative process of adjusting the model parameters to minimize the loss • Incorrect - It refers to the preprocessing of the input data before it is fed into the ML model • Incorrect - The process of adjusting the model parameters based on the loss function to maximize the loss Incorrect - It refers to the rules based on which we will make our predictions using our model
Short questions. Please choose the right choices for each question. There may be more than one correct choice. 1. A machine learning model usually has a few hyper-parameters to tune. Which of the two following settings are good for choosing hyper-parameters that can generalize well to new data: (a) Choose hyperparameters that work best on the all data (b) Choose hyperparameters that work best on the training set (c) Choose hyperparameters that work best on the validation set (d) Choose hyperparameters by performing k-fold cross-validation.
Question 8 1 You are given a dataset consisting of images of various types of animals with labels "cat", "dog", "horse, and "pin Among the supervised learning models, which one of the following would be the most appropriate to impleme try to learn these labels and predict the labels of future images? K-means clustering Polynomial regression O Binary logistic regression Linear regression Multiclass(Softmax) logistic regression Přincipal component analysis (PCA) « Previous Quiz saved at 1
Given a dataset with 100 attributes, is it true that the classification performance is more accurate on the testing dataset if we use more attributes to split the dataset and why? Please propose two methods to restrict the decision tree models in order to avoid overfitting.
In this section we create the model object and define the loss function and optimizer. For the loss function use nn.CrossEntropyLoss() instead of the mean-squared loss done in class. Use the same optimizer as in class. learning_rate = 0.001 num_epochs = 5 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #this is to use gpu if available # **create your neural net model here, call the variable 'model'** # ** complete following lines of code to define loss function and optimizer #criterion = **complete this** #optimizer = **complete this**
You learn a decision tree (DT) and a naïve Bayes (NB) model for a data set and use 10-fold cross validation to evaluate them. Suppose that DT has higher accuracy than NB on 7 folds and NB has higher accuracy on 3 folds. a. Based on this result, is one model clearly better than the other? (Yes / No) b. How can you verify with confidence which is better?
In a linear classifier, there can be multiple linear models that can perfectly classify the training data points. SVM would prefer a model that has maximum margins from the data points between both classes. Select one: A. True B. False
Question 28 Given the following information, compute a perceptron weight update for the second weight (new value for the second weight after this data point is processed during the learning of the model): Eta (learning rate): 0.1 Weights: [-1, 1, 0.1, 0.9] Current training data point: [3, 1, -0.9, 1] True label: 1