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feat: add suffixes to models to indicate their task #588

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docs: fix broken references
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lars-reimann committed Mar 30, 2024
commit 96f48f1d523124f5b4270ff07aef5802b4a7ef4d
6 changes: 3 additions & 3 deletions docs/glossary.md
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ It classifies the predictions to be either be [true positive](#true-positive-tp)
## 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].
Implemented in Safe-DS as [DecisionTreeClassifier][safeds.ml.classical.classification.DecisionTreeClassifier] and [DecisionTreeRegressor][safeds.ml.classical.regression.DecisionTreeRegressor].

## F1-Score
The harmonic mean of [precision](#precision) and [recall](#recall). Formula:
Expand All @@ -48,7 +48,7 @@ It is analogous to a column within a table.
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].
Implemented in Safe-DS as [LinearRegression][safeds.ml.classical.regression.LinearRegressionRegressor].

## Machine Learning (ML)
Machine Learning is a generic term for artificially generating knowledge through experience.
Expand Down Expand Up @@ -84,7 +84,7 @@ See here for respective references:
## 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].
Implemented in Safe-DS as [RandomForestClassifier][safeds.ml.classical.classification.RandomForestClassifier] and [RandomForestRegressor][safeds.ml.classical.regression.RandomForestRegressor].

## Recall
The ability of a [classification](#classification) model to identify all the relevant data points. Formula:
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4 changes: 2 additions & 2 deletions docs/tutorials/classification.ipynb
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Expand Up @@ -145,9 +145,9 @@
"execution_count": null,
"outputs": [],
"source": [
"from safeds.ml.classical.classification import RandomForest\n",
"from safeds.ml.classical.classification import RandomForestClassifier\n",
"\n",
"model = RandomForest()\n",
"model = RandomForestClassifier()\n",
"fitted_model= model.fit(tagged_train_table)"
],
"metadata": {
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4 changes: 2 additions & 2 deletions docs/tutorials/machine_learning.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -54,9 +54,9 @@
"execution_count": null,
"outputs": [],
"source": [
"from safeds.ml.classical.regression import LinearRegression\n",
"from safeds.ml.classical.regression import LinearRegressionRegressor\n",
"\n",
"model = LinearRegression()\n",
"model = LinearRegressionRegressor()\n",
"fitted_model = model.fit(tagged_table)"
],
"metadata": {
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4 changes: 2 additions & 2 deletions docs/tutorials/regression.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -98,9 +98,9 @@
"execution_count": null,
"outputs": [],
"source": [
"from safeds.ml.classical.regression import DecisionTree\n",
"from safeds.ml.classical.regression import DecisionTreeRegressor\n",
"\n",
"model = DecisionTree()\n",
"model = DecisionTreeRegressor()\n",
"fitted_model = model.fit(tagged_train_table)"
],
"metadata": {
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