Prediction
===

The prediction analysis enables you to load a trained machine learning model and apply it to new data. It is important that the features in the new dataset have the same names as in the original dataset used for training.

### Input

#### Trained Model
This field can load a trained machine learning model. Note that the model must be created in JASP and be saved with the extension *.jaspML*.

#### Assignment Box 
- Features: In this box, the features that need to be considered for the predictions should be entered.

#### Algorithmic Setting
- Scale features: Standardizes the continuous features. Standardization ensures that values of variables from different scales range into a specific similar scale. As a result, standardizing provides numerical stability, which improves the clustering output. JASP uses the Z-score standardization of a mean of 0 and a standard deviation of 1. This option is selected by default.

#### Tables:
- Predictions for new data: Displays the predicted outcomes for the data.
  - Add features: Adds the values of the features to the table.

### Export Results
- Add predicted outcomes to data: Creates a new variable in the data set that contains the predicted values for the data.

### Output

#### Summary Table
- Method: The method used in the model.
- Specific informtion per model.
- n(Train): Number of observations in training data.
- n(New): Number of observations in new data.

#### Predictions for New Data
- Row: The row number.
- Predicted: The predicted value / label.
