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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
3rd Edition
ISBN: 9781118729274
Author: Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Publisher: WILEY
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Students have asked these similar questions
If we add more independent variables into the model:
A.
The adjusted R2 value will increase.
B.
The R2 value will increase.
C.
The R2 value will decrease if the variables we are adding into the model should not be there.
D.
The R2 will be biased.
Question # 2: Consider the following data Classification Model where YACT is your actual observation and YPRED is the model prediction value. You have to use the data and find CONFUSION MATRIX, and using confusion matrix compute the value of the following errors:
Precision value of each class
Recall value of each class
F-measure value of each class
Model Accuracy
Model Precision value
Model Recall value
Model F-measure value
YACT
R
G
B
B
R
G
R
R
G
G
B
B
R
R
G
B
R
B
G
R
B
G
B
R
R
B
B
G
G
G
YPRED
R
R
R
B
B
B
R
R
G
B
B
R
R
G
G
G
B
B
R
R
B
G
B
R
B
G
B
G
R
R
What is the best way to decide how many epochs of training to
perform?
It is always obvious looking at the decision boundary when the
model begins to overfit.
None of the others.
As soon as the value of the Testing dataset performance begins
to decrease.
As soon as the value of the Tuning dataset performance begins
to decrease.
As soon as the value of the Training dataset performance
(accuracy, F1.) begins to decrease.
As soon as the value of the Testing dataset loss begins to
increase.
As soon as the value of the Tuning dataset loss begins to
increase.
As soon as the value of the Training dataset loss begins to
increase.
Chapter 5 Solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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- The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate the performance of the final model after selecting the hyper-parameters) Group of answer choices True Falsearrow_forwardFollowing Exhibit 5, why does the bias error decline as the model becomes more complex?arrow_forwardImagine we have two separate models, Model 1 and Model 2. The R 2 for Model 1 is 0.8 and the R 2 for Model 2 is 0.4. A. Model 1 is a better model than Model 2 B. Model 2 is a better model than Model 1 C. R2 is neither necessary nor sufficient for analysis to be usefularrow_forward
- Assume an attribute (feature) has a normal distribution in a dataset. Assume the standard deviation is S and the mean is M. Typically: Group of answer choices, multiple choice: Then the outliers usually lie below -3*M or above +3*M Then the outliers usually lie above -3*S or below +3*S Then the outliers usually lie below -3*S or above +3*S Then the outliers usually lie above -3*M or below +3*Marrow_forwardWhat are some creative ways to use binary variables in model formulation?arrow_forwardPlot following curves in the SAME figure where x-axis is "Model Complexity" and y-axis is the value: (1) Bias; (2) Variance; (3) Training Accuracy; (4) Testing Accuracy; [Paste your plot here]arrow_forward
- Explain the difference in the use of VAR models to perform forecasting and structural inferencing.arrow_forwardThe data normalization process is one of the pre-processing stages needed to improve model performance. Even so, some machine learning models use calculations that pay attention to the unit of predictor variables used so that the normalization process is something that must be done. From the models under which model requires data normalization process? a) Multiple Linear Regression] b) Random Forest c) Binomial Logistic Regression d) k-NNarrow_forwardDraw logistic regression flow chart First design the question (choose x1 and x2 values for each class (blue and red) or initialize weight and bias),then find proper weight and bias values of green line that can be seen in Figure 1. This line should separate two classes.arrow_forward
- Suppose you performed a regression analysis. You were given four observations of the target at [1.0, 1.5, 2.8, 3.7], and you predicted the values of [1.1, 1.3, 3.2, 3.7], respectively. Compute the MSE for this scenario. Perform your calculation on paper, and submit a picture of your work. This calculation should only require a couple of lines. (Some people have difficulties uploading a picture. If this happens for you, then it is acceptable to type your solution directly into the textbox, but be sure to shown enough of your work!) You MUST box-in your final answer and label the computed value as "MSE = <value>". Be sure your work is clear and legible in the photo.arrow_forwardFor finding the best fitting between model and data we have to set the best model. (a)variable (b) constant (C)error (d)parameters.arrow_forwardThis is a coding question. Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showcase how well your model predicts against the observations. Use scatter plot tor observations, line plot for your model predictions. Observations are in color red, and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot. You may also need to fine tune hyperparameters such as leurning rate and the number of iterations.arrow_forward
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