docs: glossary for important terms (#292)

Closes #188.

### Summary of Changes

Configured Snippets extension in mkdocs.yml
Created includes/glossary.md
Filled Glossary with various terms

---------

Co-authored-by: guempelp0 <[email protected]>
Co-authored-by: Lars Reimann <[email protected]>

docs/glossary.md

@@ -0,0 +1,139 @@
+# Glossary
+
+## Accuracy
+The fraction of predictions a [classification](#classification) model has correctly identified. Formula:
+
+$$
+\text{accuracy} = \frac{\text{True Positives + True Negatives}}{\text{Total amount of data points}}
+$$
+
+See here for respective definitions:
+[True Positives](#true-positive-tp)
+[True Negatives](#true-negative-tn)
+
+## Application Programming Interface (API)
+An API allows independent applications to communicate with each other and exchange data.
+
+## Classification
+Classification refers to dividing a data set into multiple chunks, which are then considered "classes".
+
+## Confusion Matrix
+A confusion matrix is a table that is used to define the performance of a [classification](#classification) algorithm.
+It classifies the predictions to be either be [true positive](#true-positive-tp), [true negative](#true-negative-tn),
+[false positive](#false-positive-fp) or [false negative](#false-negative-fn).
+
+## Decision Tree
+A Decision Tree represents the process of conditional evaluation in a tree diagram.
+
+Implemented in Safe-DS as [Decision Tree][safeds.ml.classical.classification.DecisionTree].
+
+## F1-Score
+The harmonic mean of [precision](#precision) and [recall](#recall). Formula:
+
+$$
+f_1 = 2 \cdot \frac{\text{precision} \cdot \text{recall}}{\text{precision} + \text{recall}}
+$$
+
+## False Negative (FN)
+An outcome is considered to be a false negative, if the data model has mistakenly predicted a value of negative class.
+
+## False Positive (FP)
+An outcome is considered to be a false positive, if the data model has mistakenly predicted a value of positive class.
+
+## Feature
+Each feature represents a measurable piece of data that can be used for analysis.
+It is analogous to a column within a table.
+
+## Linear Regression
+Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable
+i.e. it finds the linear relationship between the dependent and independent variable.
+
+Implemented in Safe-DS as [LinearRegression][safeds.ml.classical.regression.LinearRegression].
+
+## Machine Learning (ML)
+Machine Learning is a generic term for artificially generating knowledge through experience.
+To achieve this, one can choose between a variety of model options.
+
+## Metric
+A data metric is an aggregated calculation within a raw dataset.
+
+## One Hot Encoder
+If a column's entries consist of a non-numerical data type, using a One Hot Encoder will create
+a new column for each different entry, filling it with a '1' in the respective places, '0' otherwise.
+
+Implemented in Safe-DS as [OneHotEncoder][safeds.data.tabular.transformation.OneHotEncoder].
+
+## Overfitting
+Overfitting is a scenario in which a data model is unable to capture the relationship between the input and output variables accurately,
+due to not generalizing enough.
+
+## Positive Class
+The "Positive Class" consists of all attributes to be considered positive. Consequently, every attribute to not be in this class is considered to be negative class.
+
+## Precision
+The ability of a [classification](#classification) model to identify only the relevant data points. Formula:
+
+$$
+\text{precision} = \frac{\text{True Positives}}{\text{True Positives + False Positives}}
+$$
+
+See here for respective references:
+[True Positives](#true-positive-tp)
+[False Positives](#false-positive-fp)
+
+## Random Forest
+Random Forest is an ML model that works by generating decision trees at random.
+
+Implemented in Safe-DS as [RandomForest][safeds.ml.classical.regression.RandomForest].
+
+## Recall
+The ability of a [classification](#classification) model to identify all the relevant data points. Formula:
+
+$$
+\text{recall} = \frac{\text{True Positives}}{\text{True Positives + False Negatives}}
+$$
+
+See here for respective references:
+[True Positives](#true-positive-tp)
+[False Negatives](#false-negative-fn)
+
+## Regression
+Regression refers to the estimation of continuous dependent variables.
+
+## Regularization
+Regularization refers to techniques that are used to calibrate machine learning models
+in order to minimize the adjusted loss function and prevent [overfitting](#overfitting) or [underfitting](#underfitting).
+
+## Sample
+A sample is a subset of the whole data set.
+It is analyzed to uncover the meaningful information in the larger data set.
+
+## Supervised Learning
+Supervised Learning is a subcategory of ML. This approach uses algorithms to learn given data.
+Those Algorithms might be able to find hidden meaning in data - without being told where to look.
+
+## Tagged Table
+In addition to a regular table, a Tagged Table will mark one column as tagged, meaning that
+an applied algorithm will train to predict its entries. The marked column is referred to as ["target"](#target).
+
+## Target
+The target variable of a dataset is the feature of a dataset about which you want to gain a deeper understanding.
+
+## Test Set
+A set of examples used only to assess the performance of a fully-specified [classifier](#classification).
+
+## Training Set
+A set of examples used for learning, that is to fit the parameters of the [classifier](#classification).
+
+## True Negative (TN)
+An outcome is considered to be a true negative, if the data model has correctly predicted a value of negative class.
+
+## True Positive (TP)
+An outcome is considered to be a true positive, if the data model has correctly predicted a value of positive class.
+
+## Underfitting
+Underfitting is a scenario in which a data model is unable to capture the relationship between the input and output variables accurately,
+due to generalizing too much.
+
+## Validation Set
+A set of examples used to the parameters of a [classifier](#classification).

mkdocs.yml

@@ -11,6 +11,7 @@ nav:
     - Data Visualization: tutorials/data_visualization.ipynb
     - Machine Learning: tutorials/machine_learning.ipynb
   - API Reference: reference/
+  - Glossary: glossary.md
   - Development:
     - Environment: development/environment.md
     - Guidelines: development/guidelines.md
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