Unsupervised learning
This parameter in K-means clustering decides how many centroids to randomly place over the dataset
What is K?
Using this technique to fill in missing data artificially inflates the center of our histogram, and undervalues the variance of our dataset.
What is imputing the mean (or median)?
This type of recommender system tries to match a users to similar users.
What is user-based?
True or False, when forecasting further out from our dataset the accuracy increases.
What is False? The accuracy generally decreases as we move further away from our dataset.
Rather than calculating how likely an event is to happen, we calculate how likely an event is to happen given THIS
What is a PRIOR?
This preprocessing trick is required on all clustering models.
What is Scaling?
This distance algorithm is best described as following a grid pattern of short right angles.
What is Manhatten/taxi/l1?
This describes the term for when fluctuations occur over set intervals of time.
What is seasonality?
The result of our bayesian inference.
What is a Posterior?
This algorithm works well on oddly-shaped clusters
What is DBSCAN?
When using PCA we make these two assumptions about our data.
What is,
This vector has a cosign angle of at or near 90 degrees
What is orthogonal?
The amount of correlation between a variable and a lag of itself that is not explained by more recent correlations.
What is Partial Auto Correlation?
True or False. Giving drug A to all weekend appointments and drug B to all weekday appointments is a true experiment.
What is False?
This evaluation metric uses the sum of squared errors
What is inertia?
This is the difference between feature extraction vs feature elimination.
A cosine similarity of .99 and a pairwise distance of .01 indicates this.
What is these two things are VERY similar?
This algorithm uses lagged time series values in a regression equation.
What is ARIMA? (or SARIMA or SARIMAX or AR)
Our prior and posterior distributions are the same or can be used together easily.
What is Conjugacy?
In DBScan, this defines the distance from which to group neighboring data points.
What is Epsilon?
The error in this process:
What is fit on y? (PCA is unsupervised)
Early iterations of a user-based recommender have too few ratings to recommend effectively.
What is the cold start problem?
DAILY DOUBLE! Wager up to 1k!
Describe Benford's Law.
This word vectorizer finds similarities to words from a pre-trained model by populating sparse matrix.
What is Word2Vec?