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WorksheetsFinTech 11-1 Classification
Total questions: 12
Worksheet time: 6mins
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)
Which are classification applications?
Housing prices
Credit risk and worthiness
Fraud detection
Predicting volatility
Which models were used for classification?
Linear Regression
Logistic Regression
Support Vector Machines
Random Forests
How many datasets does the function train_test_split() create?
2
3
4
It depends on the parameters
The ratio of correctly predicted positive observations is known as:
Precision
Accuracy
Bias
Recall
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
Which elements will you see in a classification report?
precision
recall
R2 value
F1-score
Analogy - X : features :: y : ______
Dependent variable
Predictions
Target
Result
What are the expected predictions of a logistic regression classifier?
Values between -1 and 1
Categories
Values in the range of the features
Values between 0 and 1
Where does a SVM draw its classification line using a linear kernel?
A best fit through the points
Equidistant between the clusters
Across the margin of separation
Through the kernel
How was Classification day 1 for you?
Any suggestions for improvement?
