The Prediction Report has a Technical Specifications tab which gives you detailed insights regarding the algorithm, columns used, model performance and accuracy metrics.
The Tech Specs contain the following Information:
Details of the Identifier and Prediction Column.
Identifier Column is used to identify an entity/row. For example, if the dataset consists of different customers, the identifier column would be Customer ID. Prediction Column is basically the target variable that you want to predict.
List of columns which were used for the analysis.
Columns which are irrelevant and are discarded from the analysis and predictions.
The algorithm used for prediction. Obviously AI tries out several state-of-the-art Machine Learning algorithms and chooses the best one for you.
Performance of the model. This tells us whether the model is Excellent, Good, Moderate, or Bad.
Training, validation, and testing accuracy.
Percentage of the Data used for Training and testing. Training/Testing split 80/20, meaning 80% of the total data was used for training the model and 20% was used to test the performance of the model.
Time taken to train the model.
Loss Function used.
Total Rows and columns in the dataset and the number of rows and the columns that were dropped from the dataset.