You hear a lot about machine learning these days. But how does it actually work?
Take the quiz—just 10 questions—to see how much you know about machine learning!
What type of machine learning algorithm makes predictions when you have a set of input data and you know the possible responses?
What category of machine learning algorithm finds patterns in the data when the data is not labeled?
When would you reduce dimensions in your data?
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
What does a classification model do?
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
Which one of these classification algorithms is easiest to start with for prediction?
Bagged decision tree
What does hyperparameter tuning do?
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
Which feature selection technique uses shrinkage estimators to remove redundant features from data?
Sequential feature selection
Neighborhood component analysis
What is principal component analysis?
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
What is overfitting?
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
What kind of table compares classifications predicted by the model with the actual class labels?
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