Choose the answers that best complete the statements below. 1. In a neural network, increasing the number of neurons will [Select] the variance. 2. For a logistic regression model with L2 regularization, higher means higher [Select] 3. In machine learning, higher variance means that the model is [Select] 4. Non-parametric models generally tend to have higher [Select] V the bias and [Select] than parametric models.

Choose the answers that best complete the statements below. 1. In a neural network, increasing the number of neurons will [Select] the variance. 2. For a logistic regression model with L2 regularization, higher means higher [Select] 3. In machine learning, higher variance means that the model is [Select] 4. Non-parametric models generally tend to have higher [Select] V the bias and [Select] than parametric models.

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

See similar textbooks

Similar questions

Should I always treat outliers before building my machine learning model or just leave the outlier in my data
Present 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?
Apply the logistic regression with gradient descent and show only the first iteration of the algorithm. Use the learning rate =0.5, WO = 0.25. W1=2.5, W2= -3.5, W3=2.5, W X= wo + W1X1 + W2X2 + W3X3 and assume no regularization is used. Find the value of cost function and write the complete form of the hypothesis at the end of the first iteration. No.of.Purchases Is the Product Popular? No.of.Clicks No.of.SavedWishlists ProductiD (X1) (X2) (X3) P101 10 20 Yes P102 15 15 No P103 20 8 Yes P104 15 10 Yes P105 50 10 No P106 20 10 2 No P107 8 15 10 No
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?
Using the Rectified Linear Unit (ReLU) non-linear transformation function in a single hidden layer neural network with 9 hidden units for a regression task. The third hidden unit has a linear predictor value equal to 5. What is the value of the derivative of the hidden unit response with respect to the linear predictor for the third hidden unit when the linear predictor is 3 with a ReLU activation function?
Now suppose that you have two versions of your model with different parameters(e.g., different regularization) or even different model families (e.g., logistic regression versus random forest). Which one is better?
A) What is regression? B) How we can use it to estimate relationship between dependent and independent variables? C) How we can use it in machine learning?
Justify your preference for one machine learning model over another by providing illustrative examples. K-nearest neighbour analysis (a), historical data (b), and supplemental data (c, d) are two distinct methods of classifying items.
Subject: Machine Learning Given the neural network below, calculate and show the weight changes that would be made by one step of BACKPROPAGATION for the training instance (X1,X2)=(0,05,0.10) and (Y1, Y2)=(0.01,0.99). Assume that the hidden (H1 & H2) and output (Y1 & Y2) units use sigmoid functions, the network is being trained to minimize squared error, and the learning rate is 1. Edges from constant b1 and b2 on the side indicate bias parameters.
Please answer each of the following questions in detail and provide examples for better clarity, wherever applicable. Provide in-text citations. Figure 1 shows a typical composition of neurons in a net. This discussion questions aimed at learning how natural composition of neural nets can be mimicked to derive algorithms that can provide prediction at higher levels of complexity. Figure 1 is attached (Figure 1 : Typical Composition of Neurons in a Net) Consider a multiple regression model of your choice containing two predictors. Calculate the values of the regression equation with a range chosen by you for the values of the predictors. Provide the graph of the regression equation which is a plane. Augment your regression model by a sigmoid posterior filter. Calculate the values of the regression equation within the range chosen in part 1. Provide the graph of the regression equation, which will be a sigmoidal surface. Show that this graph cannot have a local extremum. Now, add…
Suppose you have implemented a loss with the regularization term for a regression task to predict what items customers will purchase on a web shopping site. However, when you test your model on a new set of customers, you find that it makes unacceptably large errors in its predictions. Furthermore, the model performs poorly on the training set. Which of the following might be promising steps to take? Check all that apply. O Try increasing the number of layers of neural model. Try evaluating your model on validation set rather than the test set. O Use fewer training examples. O Try decreasing the regularization parameter lambda
Match each the following example datasets (X,y) on the left to the most logical type of supervised learning model to use on that dataset. Here, X is the input/predictors/features of the data while y is the output/target: X = (moms height in centimeters, dad's (child's [ Choose ] height in centimeters); y = height in centimeters) [ Choose ] multivariate linear regression X = (age in years, income in USD, years of experience); y = (hired? true/false) multiclass logistic regression %3D binary logistic regression X = (number of years experience driving); y = (number of car accidents linear classification %3D involved in -- Hint: this can be logistic polynomial regression assumed as continuous in this problem due to large amount) univariate linear regression multiclass polynomial regression X = (age, income, level of agreement with various political positions); y = %3D T Choose ] (Which of 4 candidates for whom they plan to vote)