Get Prediction Model Details Response {


models (
array[Models]
)

The details of the specified prediction models.

}


Get Prediction Model Details Response:Models {


model_name (
string
)

The name of the model.


date_created (
string
)

The date when this model was trained.


model_type (
string
)

The type of the trained model.


structure (
array[Structure]
, optional)

The structure of the training data of the prediction model.


performance_measures (
Performance_measures
, optional)

The details of the algorithm and performance measures of the model.

}


Get Prediction Model Details Response:Models:Structure {


name (
string
)

The name of the field.


order (
string
)

The numeric index of the field in the data set.


type (
string
)

The type of the field.


properties (
Properties
, optional)

Some additional properties of the field.

}


Get Prediction Model Details Response:Models:Structure:Properties {


label (
boolean
, optional)

Indicates whether the field is a label.

}


Get Prediction Model Details Response:Models:Performance_measures {


selected_algorithm (
string
)

The name of the algorithm that this prediction model uses.


algorithm_params (
array[Algorithm_params]
)

The parameters used to train the model.


prediction_field (
string
)

The name of the field which this model was trained to predict.


classification_measures (
Classification_measures
, optional)

The performance measures for a classification model.


regression_measures (
Regression_measures
, optional)

The performance measures for a regression model.


selection_strategy (
string
, optional)

The selection strategy which indicates whether the model is selected by the most accurate or the most precise one.

}


Get Prediction Model Details Response:Models:Performance_measures:Algorithm_params {


param_name (
string
, optional)

The name of the parameter.


param_value (
string
, optional)

The value of the parameter.

}


Get Prediction Model Details Response:Models:Performance_measures:Classification_measures {


accuracy (
number
)

A statistical measure of how well the classification model performs on the test data. For more information see: https://en.wikipedia.org/wiki/Accuracy_and_precision


precision (
number
)

A measure that indicates the proportion of true positives out of all the results that the model identifies as positive. For more information see: https://en.wikipedia.org/wiki/Precision_and_recall#Definition_.28classification_context.29


recall (
number
)

A measure that indicates the proportion of positives that are correctly identified. For more information see: https://en.wikipedia.org/wiki/Precision_and_recall#Definition_.28classification_context.29


f_measure (
number
)

A measure of the accuracy of the model, which uses the harmonic mean of the precision and recall. For more information see: https://en.wikipedia.org/wiki/F1_score


confusion_matrix (
array[Confusion_matrix]
)

A table layout that shows the performance of the model on the test data.


train_accuracy (
number
)

A statistical measure of how well the classification model performs on the training data. For more information see: https://en.wikipedia.org/wiki/Accuracy_and_precision.


overfitting (
boolean
)

Whether the model describes random error or noise instead of the underlying relationship in the data. If overfitting is true, the model describes error or noise. For more information see: https://en.wikipedia.org/wiki/Overfitting


underfitting (
boolean
)

Whether the model successfully modelled the training data. If underfitting is set to true, the model is not a successful predictor. For more information see: https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

}


Get Prediction Model Details Response:Models:Performance_measures:Classification_measures:Confusion_matrix {


predicted_label (
string
)

The predicted label.


actual_label (
string
)

The actual label.


amount (
number
)

The number of values that have the specified predicted_label and actual_label values.

}


Get Prediction Model Details Response:Models:Performance_measures:Regression_measures {


overfitting (
boolean
)

Whether the model describes random error or noise instead of the underlying relationship in the data. If overfitting is true, the model describes error or noise. For more information see: https://en.wikipedia.org/wiki/Overfitting


mean_squared_error (
number
)

The mean square error of the regression model on the test data. For more information see: https://en.wikipedia.org/wiki/Mean_squared_error


root_mean_squared_error (
number
)

The root mean square error of the regression model on the test data. For more information see: https://en.wikipedia.org/wiki/Rootmeansquare_deviation


mean_absolute_error (
number
)

The mean absolute error of the regression model on the test data. For more information see: https://en.wikipedia.org/wiki/Mean_absolute_error


coefficient_of_determination (
number
)

A statistical measure that indicates the proportion of the variance in the target variable that is predictable from all other variables on the test data. For more information see: https://en.wikipedia.org/wiki/Coefficient_of_determination


train_mean_squared_error (
number
)

The mean square error of the regression model on the training data. For more information see: https://en.wikipedia.org/wiki/Mean_squared_error


train_root_mean_squared_error (
number
)

The root mean square error of the regression model on the training data. For more information see: https://en.wikipedia.org/wiki/Rootmeansquare_deviation


train_mean_absolute_error (
number
)

The mean absolute error of the regression model on the training data. For more information see: https://en.wikipedia.org/wiki/Mean_absolute_error


train_coefficient_of_determination (
number
)

A statistical measure that indicates the proportion of the variance in the target variable that is predictable from all other variables on the training data. For more information see: https://en.wikipedia.org/wiki/Coefficient_of_determination

}

