Make predictions

conode makes it easy to run fast predictions on your data.

The "Fit" agent will fit either a linear regression or logistic regression depending on the target data type. It assumes all given data is training data. Upon completion, a set of views explaining the model performance and score will be opened.

Both linear and logistic regression will open 3 new views. A summary view, a weights view and an evaluation view.

• The summary view just shows the model input and out. It is worth highlighting that this view contains a header node with the evaluation of the model on the training data.
• The weights view shows the weight associated with each input feature vector towards the result.
• The evaluation view is used to evaluate the model performance. It is a plot of true vs predicted target feature vector.

If predicting categorical data, the model used is a multinomial logistic regression. Therefore, there is a weight per input feature and output category. For example, predicting the shirt size (S, M, L, XL) given the height of a person, the model will produce a weight for each target category S, M, L, XL.