# Glossary

We're going to learn many terms and concepts this semester. This page catalogs many of the important ones, with pointers to the resources in which they are introduced.

Ablation study

A study in which we turn off different components of a complex model to see how much each one contributes to the overall model's performance.

Introduced in Inference and Albation.

Aggregate

A function that computes a single value from a series (or matrix) of values. Often used to compute a statistic.

Introduced in Groups and Aggregates.

Bayesianism
A school of thought for statistical inference and the interpretation of probability that is concerned with using probability to quantify uncertainty or coherent states of belief. In statistical inference, this results in methods that quantify knowledge with probability distributions, and update those distributions based on the results of an experiment or data analysis. Not to be confused with Bayes' Theorem, which is a fundamental building block of Bayesian inference but has many other uses as well.
Bayes' Theorem

A theorem or identity in probability theory that allows us to reverse a conditional probability:

$P(B|A) = \frac{P(A|B) P(B)}{P(A)}$

Statisticians of all schools of thought make use of Bayes' theorem — all it does is relate $$P(A|B)$$ to $$P(B|A)$$.

Introduced in Joint and Conditional Probability.

Bootstrap

A technique for estimating sampling distributions by repeatedly resampling the available sample with replacement.

Introduced in The Bootstrap.

Central limit theorem
The theorem that describes the sampling distribution of the sample mean. If we take a random sample $$X$$ from (most) populations with mean $$\mu$$ and variance $$\sigma^2$$, the sample mean $$\bar{x} \sim \mathrm{Normal}(\mu, \sigma/\sqrt{n})$$.
Classification
A problem where the goal is to predict a discrete class for an instance. This is often binary classification, where instances are categorized into one of two classes.
Conditional Probability

The conditional probability $$P(B|A)$$ (read “the probability of $$B$$ given $$A$$”) is the probability of $$B$$, given that we know $$A$$ occured. We can also discuss conditional expectation $$\mathrm{E}[X|A]$$, the expected value of $$X$$ for those occurrances where $$A$$ occurred.

Introduced in Joint and Conditional Probability.

Confidence Interval

An interval used to estimate the precision of an estimate. A 95% confidence interval is an interval computed from a procedure (including both taking a sample and computing a statistic from that sample) that, when repeated, will return an interval containing the true parameter value 95% of the time. Discussed in Confidence, Having confidence in confidence intervals, and Handbook section 1.3.5.2.

A confidence interval is not a probabilistic statement about either the population mean $$\mu$$ or the sample mean $$\bar{x}$$.

Correlation

The extent to which two variables change with each other. If one variable usually increases when the other one increases, the variables are correlated; if one decreases when the other increases, they are anticorrelated.

Correlation is measured with the correlation coefficient:

$r = \frac{\sum(x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum(x_i - \bar{x})^2}\sqrt{\sum(y_i - \bar{y})^2}}$

This is equivalent to the covariance scaled by the standard deviations of the variables:

$\mathrm{Cor}(X, Y) = \frac{\mathrm{Cov}(X, Y)}{\sigma_X \sigma_Y}$
Degrees of Freedom

The number of observations in a series that can independently vary to affect a calculation. This is usually the number of observations, minus the number of intermediate statistics. For example, the degrees of freedom for the sample standard deviation for $$n$$ observations is $$n-1$$, because one DoF is “used up” by the mean:

$s = \sqrt{\frac{\sum_i (x_i - \bar{x})^2}{n - 1}}$
Elementary Event

In probability theory, an individual distinct outcome of a process we are modeling as random.

Introduced in Probability.

Embedding

As a noun, a vector-space representation of a data point or instance. This is often a lower-dimensional representation produced through some form of matrix decomposition such as SVD.

As a verb, to convert an instance to such a representation.

Environment variable

A string variable associated with a process by the operating system. Often used for configuring the behavior of software, such as the number of threads to use in parallel computation. Child processes inherit their parents' environment variables.

Environment variables for the current process can be accessed and set in Python via the dictionary os.environ.

In the Unix shell, set an environment variable with:

export MY_VAR="contents"


In PowerShell, set it with:

\$env:MY_VAR="contents"


Set an environment variable before running commands that need to be governed by it.

Euclidean norm
See L₂ Norm
Event

In probability theory, an outcome that for which we want to estimate the probability. Formally, given a set $$E$$ of elementary outcomes, an event is a set $$A \subseteq E$$, and the set of possible events $$\mathcal{F}$$ forms a sigma field.

Introduced in Probability.

Estimand
An unknown quantity that we try to estimate. See Estimator.
Estimate
n. A value computed to approximate the value of some estimand. See Estimator.
v. The process of computing an estimate for an estimand.
Estimator

A computation (or computed value) that we use to try to estimate an unknown value. Formally, an estimator is a computation to produce an estimate of an estimand. The sample mean $$\bar{x}$$, as an abstract concept, is an estimator of the population mean, also as an abstract concept. Any particular sample mean we compute, such as $$\bar{x} = 3.2$$, is an estimate of the population mean for that sample.

Introduced in Inference Introduction.

Expected value
The mean of a random variable $$X$$: $$\E[X] = \sum x P(x)$$ or $$\E[X] = \int x p(x) dx$$.
Frequentism
A school of thought for statistical inference and the interpretation of probability that is concerned with probabilities as descriptions of the long-run behavior of a random process: how frequent would various outcomes be if the process were repeated infinitely many times? In statistical inference, this results in methods that are characterized by their behavior if a sampling procedure or experiment were repeated, such as confidence intervals (defined in terms of the behavior of calculating them over multiple samples) and p-values (the probability that a random sample would produce a statistic at least as large as the observed statistic if the sampling procedure were repeated).
Hyperparameter
A value that controls a model's training or prediction behavior that is not learned from the data. Examples include learning rates, iteration counts, and regularization terms.
Joint Probability

The joint probability $$P(A, B)$$ is the probability of both $$A$$ and $$B$$ occurring (in terms of underlying events, it's the probability that the elementary event $$\zeta$$ is in both $$A$$ and $$B$$). Equivalent to $$P(A \cap B)$$. Related to the conditional and marginal probabilities by $$P(A, B) = P(A|B) P(B)$$. Symmetric ($$P(A, B) = P(B, A)$$).

Introduced in Joint and Conditional Probability.

L₁ Norm

A measure of the magnitude of a vector, sometimes called the Manhattan distance. It is the sum of the absolute values of the elements in the vector:

$\| \mathbf{x} \|_1 = \sum_i |x_i|$
L₂ Norm

A measure of the magnitude of a vector, also called the Euclidean norm or Euclidean length. It is square root of the sum of squares of the elements in the vector:

$\| \mathbf{x} \|_2 = \sqrt{\sum_i x_i^2}$
Leakage
When your predictive model benefits from information that would not be available when the model is in actual use. Setting aside test data until the model is ready for final evaluation helps reduce leakage.
Linear model

A model of the form $$\hat{y} = \beta_0 + \sum_i \beta_i x_i$$: it is the sum of scalar products.

Linear models are introduced in Week 8.

Logistic function

A sigmoid function that maps unbounded real values to the range $$(0,1)$$:

$\mathrm{logistic}(x) = \frac{1}{1 + e^{-x}} = \frac{e^x}{e^x + 1}$

The logistic function is the invert of the logit function.

Logistic regressions are introduced in Week 10.

Logit function

The inverse of the logistic function:

$\mathrm{logit(x)} = \mathrm{logistic}^{-1}(x) = \operatorname{log} \frac{x}{1-x} = \operatorname{log} x - \operatorname{log} (1-x)$

Applying logit to a probability yields the log odds.

Majority-class classifier
A classifier that classifies every data point with the most common class from the training data. If 72% of the training data is in class A, the majority-class classifier will classify every test point as A, no matter what its input feature values are.
Marginal Probability

The probability of a single event, or distribution of a single dimension, $$P(A)$$. Primarily used when we are talking about the probability of events (or expectation of variables) along one dimension of a product space, such as the suit or number of a card from a deck of playing cards.

Described in Joint and Conditional Probability.

Matrix
A two-dimensional array of numbers. Alternatively, a linear map between vector spaces.
Matrix decomposition

A decomposition of a matrix into other matrices, such that multiplying the decomposition back together yields the original matrix or an approximation thereof. An example is the singular value decomposition (SVD):

$M = P \Sigma Q^T$

where $$P \in \Reals^{m \times k}$$ and $$Q \in \Reals^{n \times k}$$ are orthogonal, and $$\Sigma \in \Reals^{k \times k}$$ is diagonal.

Objective Function

A function describing a model's performance that is used as the goal for learning its parameters. This can be a loss function (where the goal is to minimize it) or a utility function (which should be maximized).

Defined in Building and Evaluating Models, and introduced in Optimizing Loss.

Operationalization

The mapping of a goal or question to a specific, measurable quantity (or measurement procedure). When we operationalize a question, we translate it into the precise computations and measurements we will use to attempt to answer it.

Odds

An alternative way of framing probability, as the ratio of the likelihood for or against an event:

$\Odds(A) = \frac{P(A)}{P(A^c)}$

The log odds is a particularly convenient way of working with odds, and is $$\log P(A) - \log (1 - P(A))$$. See the probability notes.

Odds ratio

The ratio of the odds of two different outcomes.

$\operatorname{OR}(A, B) = \frac{\Odds(A)}{\Odds{B}}$

See the probability notes.

Overfitting

When a model learns too much from its training data, so it cannot do an effective job of predicting future unseen data.

Introduced in Overfitting.

Parameter

In inferential statistics: a “true” value in the population, such as the mean flipper length of Chinstrap penguins. The goal of inferential statistics is often to estimate parameters, because we typically do not have direct access to them.

Introduced in Sampling.

In model fitting: a variable in a statistical or machine learning model whose value is learned from the data. Contrast hyper-parameter, a variable that controls the model or the model-fitting process but is not learned from the data.

Population

The complete set of entities we want to study. This is not only all entities that do exist, but under some philosophies, all entities that could exist. For example, the set of all possible adult Chinstrap penguins would be the population.

Discussed in more detail in Sampling.

P-value

In hypothesis testing, the probability that the null hypothesis ($$H_0$$) would produce a value as large as the observed value. Typically the null hypothesis is an appropriate formalization of “nothing interesting”, so the p-value is the probability of seeing an effect as large as the one observed if there is no true effect to observe.

Discussed in Testing Hypotheses

Regression

A modeling or prediction problem where we try to estimate or predict a continuous variable $$Y$$.

This is the focus of Week 8.

Regularization

A penalty term added to a loss function, typically penalizing large values. Used to encourage sparsity or to require coefficients to be supported by larger quantiies of data.

Introduced in :a-video Regularization.

Residual

The error in estimating a variable with a model. For a model fitting an estimator $$\hat{Y}$$ for a variable $$Y$$, the residuals are $$\epsilon_i = y - \hat{y}$$. This is reflected in the full linear model: $$y_i = \beta_0 + \sum_j \beta_j x_{ij} + \epsilon_i$$.

Introduced in Single Regression.

Sample

n. A subset of the population, for which we have observations.

Discussed in more detail in Sampling.

Sample Size
The number of items in the sample. Often denoted $$n$$.
Sampling distribution
The distribution of a statistic when it is computed over many repeated samples of the same size from the same population. The sampling distribution of the sample mean from a population with mean $$\mu$$ and variance $$\sigma^2$$ is $$\mathrm{Normal}(\mu, \sigma/\sqrt{n})$$.
Statistic

A value computed from a set of observations. For example, the sample mean $$\bar{x} = n^{-1} \sum_i x_i$$ is a statistic of a sample $$X = \langle x_i, \dots, x_n \rangle$$.

Discussed in Inference Intro.

Standard deviation

A measure of the spread of a random variable. It is the square root of the mean squared deviation from the mean:

$\sigma_X = \sqrt{\frac{\sum_i (x_i - \bar{x})^2}{n}}$

When computing the standard deviation from a sample, we instead compute the sample standard deviation:

$s = \sqrt{\frac{\sum_i (x_i - \bar{x})^2}{n - 1}}$
Standard error

The standard deviation of the sampling distribution of a statistic. The standard error of the mean (Pandas function .sem) is $$s/\sqrt{n}$$.

Discussed in Confidence.

t-test

A statistical test for means of normally-distributed data. T-tests come in three varieties:

1. One-sample t-test that tests whether a single mean is different from zero (or another fixed value $$\mu_0$$). $$H_0: \mu=0$$
2. Two-sample independent t-test that tests whether the means of two independent samples are the same. $$H_0: \mu_1 = \mu_2$$
3. Paired t-test that tests, for a sample of paired observations, whether the mean difference between observations for each sample is zero (the measurements are, on average, the same). $$H_0: \mu_{x_{i1} - x_{i2}} = 0$$

Discussed in Testing Hypotheses, T-tests, and associated readings.

Variance

A measure of the spread of a random variable (which may be observable quantities in the population).

$\Var(X) = \E[(X - \E[X])^2]$

Variance is the square of the standard deviation, and is sometimes written $$\sigma^2$$.

Vector
A sequence or array of numbers; $$\mathbf{x} = [x_1, x_2, \dots, x_n]$$ is an $$n$$-dimensional vector.
Vectorization
Writing a computation so that mathematical operations are done across entire arrays at a time, rather than looping over individual data points in Python code.
Unbiased estimator
An estimator whose expected value is the population parameter.