1. A computer implemented hybrid method of predicting click-through rates, said method comprising: creating a machine learning based model, said machine learning model constructed using training data having a first set of features; creating a tree-structured statistical table from a second set of features in training data by applying Kalman-filter methods to a tree-structured Markov model to estimate parameters in said tree-structured statistical table; executing said machine learning model using said first set of features from test data; and adding to augment the base prediction of the executed machine learning model by an adjustment factor obtained from said tree-structured statistical table using said second set of features from said test data to predict click-through-rates.
1. A computer implemented hybrid method of predicting click-through rates, said method comprising: creating a machine learning based model, said machine learning model constructed using training data having a first set of features; creating a tree-structured statistical table from a second set of features in training data by applying Kalman-filter methods to a tree-structured Markov model to estimate parameters in said tree-structured statistical table; executing said machine learning model using said first set of features from test data; and adding to augment the base prediction of the executed machine learning model by an adjustment factor obtained from said tree-structured statistical table using said second set of features from said test data to predict click-through-rates. 3. The hybrid method of predicting click-through rates as set forth in claim 1 wherein said second set of features comprises text from web pages.