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FinTech 11-1 Classification

Authored by Louis Burns

Computers, Other

Professional Development

Used 9+ times

FinTech 11-1 Classification
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12 questions

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1.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Which were the objectives of Classification day 1?

Convert categorical data to input vectors

Define classification in the context of machine learning

Evaluate classification algorithms using a confusion matrix and classification report

Model and fit several classification models (Linear and Logistic Regression and SVM)

2.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Which are classification applications?

Housing prices

Credit risk and worthiness

Fraud detection

Predicting volatility

3.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Which models were used for classification?

Linear Regression

Logistic Regression

Support Vector Machines

Random Forests

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How many datasets does the function train_test_split() create?

2

3

4

It depends on the parameters

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The ratio of correctly predicted positive observations is known as:

Precision

Accuracy

Bias

Recall

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of a confusion matrix?

Helps to select the best model

Calculate precision and accuracy

Table that describes the performance of a classification model

To generate a classification report

7.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Which elements will you see in a classification report?

precision

recall

R2 value

F1-score

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