SEI Insights
Posted on by Machine Learning
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by Zachary T. Kurtz
Statistics Ph.D. and Machine Learning Research Scientist
CERT Division
Statistics and machine learning often use different terminology for similar concepts. I recently confronted this when I began reading about maximum causal entropy as part of a project on inverse reinforcement learning. Many of the terms were unfamiliar to me, but as I read closer, I realized that the concepts had close relationships with statistics concepts. This blog post presents a table of connections between terms that are standard in statistics and their related counterparts in machine learning.
Understanding a result in machine learning can help to avoid reinventing the wheel in statistics and vice versa. My ability to understand inverse reinforcement learning benefited from my training in statistics because I was able to translate machine learning terminology into statistical terminology. Translation takes effort, however, and my research would have proceeded even more smoothly if translation were not required. This experience motivated me to compile a table of common statistics and machine learning terms and connections between them.
Each row of the table links a set of terms that are standard in statistics literature (first column) with related concepts in machine learning (second column). Notes in the third column explain the relationship. Several disclaimers apply:
Statistics |
Machine learning |
Notes |
data point, record, row of data |
example, instance |
Both domains also use "observation," which can refer to a single measurement or an entire vector of attributes depending on context. |
response variable, dependent variable |
label, output |
Both domains also use "target." Since practically all variables depend on other variables, the term "dependent variable" is potentially misleading. |
variable, covariate, predictor, independent variable |
feature, side information, input |
The term "independent variable" exists for historical reasons but is usually misleading--such a variable typically depends on other variables in the model. |
regressions |
supervised learners, machines |
Both estimate output(s) in terms of input(s). |
estimation |
learning |
Both translate data into quantitative claims, becoming more accurate as the supply of relevant data increases. |
hypothesis ≠ classifier |
hypothesis |
In both statistics and ML, a hypothesis is a scientific statement to be scrutinized, such as "The true value of this parameter is zero." In ML (but not in statistics), a hypothesis can also refer to the prediction rule that is output by a classifier algorithm. |
bias ≠ regression intercept |
bias |
Statistics distinguishes between (a) bias as form of estimation error and (b) the default prediction of a linear model in the special case where all inputs are 0. ML sometimes uses "bias" to refer to both of these concepts, although the best ML researchers certainly understand the difference. |
Maximize the likelihood to estimate model parameters |
If your target distribution is discrete (such as in logistic regression), minimize the entropy to derive the best parameters. If your target distribution is continuous, fine, just maximize the likelihood. |
For discrete distributions, maximizing the likelihood is equivalent to minimizing the entropy. |
Apply Occam's razor, or encode missing prior information with suitably uninformative priors. |
Apply the principle of maximum entropy. |
The principle of maximum entropy is conceptual and does not refer to maximizing a concrete objective function. The principle is that models should be conservative in the sense that they be no more confident in the predictions than is thoroughly justified by the data. In practice this works out as deriving an estimation procedure in terms of a bare-minimum set of criteria as exemplified here or here. |
logistic/multinomial regression |
maximum entropy, MaxEnt |
They are equivalent except in special multinomial settings like ordinal logistic regression. Note that maximum entropy here refers to the principle of maximum entropy, not the form of the objective function. Indeed, in MaxEnt, you minimize rather than maximize the entropy expression. |
X causes Y if surgical (or randomized controlled) manipulations in X are correlated with changes in Y |
X causes Y if it doesn't obviously not cause Y. For example, X causes Y if X precedes Y in time (or is at least contemporaneous) |
The stats definition is more aligned with common-sense intuition than the ML one proposed here. In fairness, not all ML practitioners are so abusive of causation terminology, and some of the blame belongs with even earlier abuses such as Granger causality. |
structural equations model |
Bayesian network |
These are nearly equivalent mathematically, although interpretations differ by use case, as discussed. |
sequential experimental design |
active learning, reinforcement learning, hyperparameter optimization |
Although these four subfields are very different from each other in terms of their standard use cases, they all address problems of optimization via a sequence of queries/experiments. |
The bifurcation of machine learning and statistics terminology has its roots in a historical disconnect between the efforts of computer scientists and statisticians. The table in this 1983 paper shows that the resulting translation challenges have been around for a long time. I'm willing to bet that new opportunities for translation will arise for as long as the fields of statistics and machine learning continue to expand.
Additional Resources
Read the SEI Blog Post Machine Learning in Cybersecurity.
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