Curve Fit: Docs

Author: VesnaT

Orange/widgets/model/owcurvefit.py

@@ -492,7 +492,7 @@ def create_learner(self) -> Optional[CurveFitLearner]:
         return learner
 
     def get_learner_parameters(self) -> Tuple[Tuple[str, Any]]:
-        return tuple(("Function", self.expression), )
+        return ("Expression", self.expression),
 
     def update_model(self):
         super().update_model()

doc/visual-programming/source/widgets/model/curvefit.md

@@ -16,3 +16,34 @@ Fit a function to data.
 
 The **Curve Fit** widget fits an arbitrary function to the input data. It only works for regression tasks.
 The widget uses [scipy.curve_fit](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html) to find the optimal values of the parameters.
+
+The widget works only on regression tasks and only numerical features can be used for fitting.
+
+![](images/CurveFit-stamped.png)
+
+1. The learner/predictor name.
+2. Introduce model parameters.
+3. Input an expression in Python. The expression should consist of at least one fitting parameter.
+4. Select a feature to include into the expression. Only numerical features are available.
+5. Select a parameter. Only the introduced parameters are available.
+6. Select a function.
+7. Press *Apply* to commit changes. If *Apply Automatically* is ticked, changes are committed automatically.
+8. Show help, produce a report, input/output info.
+
+Preprocessing
+-------------
+
+Curve fit uses default preprocessing when no other preprocessors are given. It executes them in the following order:
+
+- removes instances with unknown target values
+- removes empty columns
+- imputes missing values with mean values
+
+To remove default preprocessing, connect an empty [Preprocess](../data/preprocess.md) widget to the learner.
+
+Example
+-------
+
+Below, is a simple workflow with *housing* dataset. Due to example simplicity we used only a single feature. Unlike the other modelling widgets, the Curve Fit needs data on the input. We trained **Curve Fit** and [Linear Regression](../model/linearregression.md) and evaluated their performance in [Test & Score](../evaluate/testandscore.md).
+
+![](images/CurveFit-example.png)