How Much Do You Know About Machine Learning?

Supervised learning

Supervisory logic

Unsupervised learning

Deep learning

Data prophecy

Unsupervised learning

Supervisory logic

Supervised learning

When the data comes from sensors

When you’re using a Linux machine

When your data set is larger than 500 GB

When you have a large set of features with similar characteristics

Predicts real number responses such as changes in temperature, date, or time

Assigns data to a predefined category

Clusters responses in groups based on similarity, to find patterns

Compares predicted data classifications to the actual class labels in the data

Naïve Bayes

Neural network

Logistic regression

Bagged decision tree

Optimizes parameters to improve performance of a learning algorithm

Specifies the hyperplane that represents linear classifiers

Expands the parameter set of a model to improve performance

Takes parameter tuning so far that performance degrades

Stepwise regression

Sequential feature selection

Neighborhood component analysis

Regularization

A feature selection technique that adds or removes features to optimize prediction accuracy

A linear feature transformation technique for reducing data dimensionality

A clustering technique that partitions data into mutually exclusive clusters

A predictive technique that identifies a better set of parameters

When a predictive model is accurate but takes too long to run

When the model learns specifics of the training data that can’t be generalized to a larger data set

When you apply a powerful deep learning algorithm to a simple machine learning problem

When you perform hyperparameter tuning and performance degrades

Chaos table

Confusion matrix

Prediction plot

Residual plot