According to the Naïve Bayes Classifier example, what can we conclude about an average customer's behavior on a holiday with discount and free delivery?

They are more likely to purchase.

They are less likely to purchase.

They are more likely to not purchase.

The probability of purchase cannot be determined.

Which of the following is an advantage of the Naïve Bayes Classifier according to the image?

Highly scalable with number of predictors and data points

Requires a large amount of training data

Very complex and difficult to implement

What does the Naïve Bayes Classifier handle according to the image?

Both continuous and discrete data

Neither continuous nor discrete data

According to the image, how does the speed of the Naïve Bayes Classifier affect its usage?

It is fast and can be used in real-time predictions

It is too slow for any practical use

Its speed varies with the size of the dataset

It is only suitable for batch processing

What is one characteristic of the Naïve Bayes Classifier related to irrelevant features as mentioned in the image?

Not sensitive to irrelevant features

Highly sensitive to irrelevant features

Moderately sensitive to irrelevant features

Requires additional algorithms to handle irrelevant features

According to the image, what is the implementation complexity of the Naïve Bayes Classifier?

Very simple and easy to implement

Requires advanced mathematical understanding

Can only be implemented by machine learning experts

Based on the image, how much training data does the Naïve Bayes Classifier need?

An excessive amount for accurate results

Cannot function without real-time data

Requires a fixed amount of data for all types of problems

What is one disadvantage of the Naïve Bayes Classifier related to its assumptions about attributes?

Its strong assumption about the independence of attributes often gives bad results (i.e., bad prediction accuracy).

It assumes a high level of correlation between attributes, leading to overfitting.

It assumes all attributes are equally important, which is not always the case.

It requires a large amount of data to make accurate predictions.

What is a potential issue when discretising numerical values in the Naïve Bayes Classifier?

Discretising numerical values may result in loss of useful information (Lower resolution).

It can lead to a higher computational cost.

It increases the complexity of the model significantly.

It can cause the classifier to become biased towards categorical variables.

Why is evaluation an important activity in Phase 4?

It allows us to determine if a chosen analytic model has performed well in meeting our analytical needs.

It helps in planning the next phase.

It is necessary for model deployment.

It is used for selecting the data sources.

What should a data scientist know concerning the evaluation of analytics models?

The differences in evaluating supervised and unsupervised models

The programming languages used to create the models

The data used to train the models

What does the False Positive Rate (FPR) indicate when assessing the performance of supervised models?

The rate at which negatives are incorrectly identified as positives

The rate at which true positives are correctly identified

The rate at which true negatives are correctly identified

The rate at which positives are incorrectly identified as negatives

Which metric is described as the "Area Under the Curve (AUC)" in the evaluation of supervised models?

The plasma concentration-time profile

The curve of true positive rate versus false positive rate

The curve of true negative rate versus false negative rate

What is a Confusion Matrix used for in the context of supervised models?

To visualize the performance of a supervised model or classifier

To calculate the total number of features in a dataset

To determine the best hyperparameters for a model

To measure the computational complexity of an algorithm

In a 2-class classifier confusion matrix, what does the term "True Positives" (TP) refer to?

The instances correctly predicted as positive

The instances correctly predicted as negative

The instances incorrectly predicted as positive

The instances incorrectly predicted as negative

What does "False Positives" (FP) stand for in a confusion matrix?

The instances incorrectly predicted as positive

The instances correctly predicted as negative

The instances correctly predicted as positive

The instances incorrectly predicted as negative

What are "True Negatives" (TN) in the context of a confusion matrix?

The instances correctly predicted as negative

The instances incorrectly predicted as positive

The instances correctly predicted as positive

The instances incorrectly predicted as negative

What does "False Negatives" (FN) mean in a confusion matrix?

The instances incorrectly predicted as negative

The instances correctly predicted as negative

The instances incorrectly predicted as positive

The instances correctly predicted as positive

What does True Positive (TP) mean in the context of a classifier's performance?

The number of positive instances correctly classified as positive.

The number of negative instances classified as positive.

The number of positive instances classified as negative.

The number of negative instances correctly classified as negative.

What does False Positive (FP) indicate in a classifier's performance?

The number of negative instances classified as positive.

The number of positive instances classified as negative.

The number of negative instances correctly classified as negative.

The number of positive instances correctly classified as positive.

What does True Negative (TN) signify in the context of a classifier's performance?

The number of negative instances correctly classified as negative.

The number of positive instances classified as negative.

The number of negative instances classified as positive.

The number of positive instances correctly classified as positive.

What does False Negative (FN) mean in terms of a classifier's performance?

The number of positive instances classified as negative.

The number of negative instances classified as positive.

The number of positive instances correctly classified as positive.

The number of negative instances correctly classified as negative.

What is the ideal condition for a good classifier in terms of False Positives (FP) and False Negatives (FN)?

Small (ideally, zero) numbers of FP and FN.

Small numbers of FP and large numbers of FN.

Large numbers of FP and small numbers of FN.

What does accuracy measure in the context of supervised models?

The rate at which a model has classified the records correctly

The rate at which a model incorrectly classifies the records

The number of true positives divided by the number of false positives

The percentage of true negatives in the dataset

Which of the following is the correct formula for calculating accuracy in supervised models?

Accuracy = (TP + TN) / (TP + TN + FP + FN) x 100%

Accuracy = (TP + FP) / (TP + TN + FP + FN) x 100%

Accuracy = (FP + FN) / (TP + TN + FP + FN) x 100%

Accuracy = (TN + FN) / (TP + TN + FP + FN) x 100%

Why might accuracy alone not be sufficient to guarantee that a model is well-established?

Because other metrics are needed to better evaluate the performance of a supervised model

Because it does not account for the balance between true positives and true negatives

Because it only considers the number of false positives

Because it may overlook the model's ability to handle new or unseen data

Data Principles week 4 part 2 Quiz

100 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

According to the likelihood table for 'Day', what is the likelihood of a customer buying on a holiday?

9/24

8/24

2/6

3/6

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the likelihood of a customer not buying when there is a discount according to the likelihood table?

1/6

5/24

5/6

1/24

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Based on the likelihood table for 'Free Delivery', what is the likelihood of a customer not buying if there is no free delivery?

4/6

3/24

4/24

2/6

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

According to the Naive Bayes Classifier example, what is the probability of not buying given that it is a holiday, there is a discount, and there is free delivery?

0.0296

0.026

0.3

0.7

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the 'No' column in the Likelihood Table represent in the context of the Naive Bayes Classifier?

The probability of buying

The probability of not buying

The frequency of buying

The frequency of not buying

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How is the likelihood of 'No Buy' for 'Discount = Yes' calculated in the Naive Bayes Classifier example?

19/24

1/6

5/24

1/20

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of the Frequency Table in the Naive Bayes Classifier example?

To calculate the total probability of buying or not buying

To determine the actual number of purchases made

To show the likelihood of buying given certain conditions

To provide raw data for calculating the likelihoods

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Data Principles week 4 part 2

According to the likelihood table for 'Day', what is the likelihood of a customer buying on a holiday?

What is the likelihood of a customer not buying when there is a discount according to the likelihood table?

Based on the likelihood table for 'Free Delivery', what is the likelihood of a customer not buying if there is no free delivery?

According to the Naive Bayes Classifier example, what is the probability of not buying given that it is a holiday, there is a discount, and there is free delivery?

What does the 'No' column in the Likelihood Table represent in the context of the Naive Bayes Classifier?

How is the likelihood of 'No Buy' for 'Discount = Yes' calculated in the Naive Bayes Classifier example?

What is the purpose of the Frequency Table in the Naive Bayes Classifier example?

According to the frequency table, how many times did customers buy on a holiday?

What is the likelihood of buying given that it is a weekday according to the likelihood table?

Based on the frequency table, how many times did customers not buy when there was no discount?

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