Common ML Problems Flashcards
What is Machine Learning?
ML is the process of training a piece of software, called a model, to make useful predictions using a data set.
What is a Model?
The representation of what a machine learning system has learned from the training data
What is an example?
One row of a dataset. An example contains one or more features and possibly a label.
What is a feature?
An input variable used in making predictions.
ML problems fall along a spectrum of supervision between _____
supervised and unsupervised learning.
What is a label?
In supervised learning, the “answer” or “result” portion of an example.
What is training?
The process of determining the ideal parameters comprising a model.
What is Supervised Learning?
Supervised learning is a type of ML where the model is provided with labeled training data.
Supervised machine learning finds patterns ___
between data and labels that can be expressed mathematically as functions.
Given an input feature, you are telling the system ___
what the expected output label is, thus you are supervising the training.
What is the goal of unsupervised learning?
the goal is to identify meaningful patterns in the data.
In unsupervised learning the machine must __
learn from an unlabeled data set. In other words, the model has no hints how to categorize each piece of data and must infer its own rules for doing so.
What is a bias?
Stereotyping, prejudice or favoritism towards some things, people, or groups over others. These biases can affect collection and interpretation of data, the design of a system, and how users interact with a system.
What is Reinforcement Learning (RL)?
is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment.
(Think of psychology)
What are the types of ML problems?
- Classification
- Regression
- Clustering
- Association Rule Learning
- Structured Output
- Ranking
