Intro to Machine Learning - Financial Technology

Flashcards reviewing key concepts from the Intro to Machine Learning - Financial Technology lecture.

What is artificial intelligence?

The simulation of any intellectual task.

What are the two main purposes for performing data analysis?

Inference and Prediction.

What is the difference between inference and prediction?

Inference seeks to find causal effects while prediction uses inputs to predict outcomes.

What are the two types of learning in machine learning?

Supervised learning and Unsupervised learning.

What is a classification problem?

A problem where the outcome variable is discrete and the goal is to predict categories.

What is a regression problem?

A problem where the outcome variable is continuous and the goal is to predict numeric values.

What statistical tool is considered the gold standard for inference?

Randomized control trials (RCT).

What does MSE stand for and how is it calculated?

Mean Square Error; it measures the average squared difference between actual and predicted outcomes.

What is a confusion matrix?

A table used to evaluate the performance of a classification model, showing true and false positives/negatives.

What is precision in terms of model accuracy?

The ratio of true positives to the sum of true positives and false positives.

What is the True Positive Rate (TPR)?

The ratio of true positives to the sum of true positives and false negatives, also known as recall.

What does the F1 score represent?

The harmonic mean of precision and recall, balancing the trade-off between the two.

What is the ROC curve used for?

To plot the True Positive Rate against the False Positive Rate for different threshold settings.

What is overfitting in machine learning?

When a model is too complex and fits too closely to a limited set of data points, reducing its ability to predict on new data.

How can we check for overfitting when building models?

Using techniques like cross-validation, including Holdout Method and K-Fold Cross Validation.

What is the bias-variance tradeoff?

The balance between a model's ability to minimize bias (error due to erroneous assumptions) and variance (error due to sensitivity to small fluctuations in training set).