FinTech 11-1 Classification

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