/// <summary>
        /// FastTree <see cref="BinaryClassificationContext"/> extension method.
        /// Predict a target using a decision tree binary classificaiton model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
        /// </summary>
        /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
        /// <param name="label">The label column.</param>
        /// <param name="features">The features colum.</param>
        /// <param name="weights">The optional weights column.</param>
        /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
        /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
        /// <param name="minDatapointsInLeafs">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
        /// <param name="learningRate">The learning rate.</param>
        /// <param name="advancedSettings">Algorithm advanced settings.</param>
        /// <param name="onFit">A delegate that is called every time the
        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
        /// the linear model that was trained. Note that this action cannot change the result in any way;
        /// it is only a way for the caller to be informed about what was learnt.</param>
        /// <returns>The set of output columns including in order the predicted binary classification score (which will range
        /// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
        public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) FastTree(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
                                                                                                                Scalar <bool> label, Vector <float> features, Scalar <float> weights = null,
                                                                                                                int numLeaves            = Defaults.NumLeaves,
                                                                                                                int numTrees             = Defaults.NumTrees,
                                                                                                                int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs,
                                                                                                                double learningRate      = Defaults.LearningRates,
                                                                                                                Action <FastTreeBinaryClassificationTrainer.Arguments> advancedSettings = null,
                                                                                                                Action <IPredictorWithFeatureWeights <float> > onFit = null)
        {
            FastTreeStaticsUtils.CheckUserValues(label, features, weights, numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings, onFit);

            var rec = new TrainerEstimatorReconciler.BinaryClassifier(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new FastTreeBinaryClassificationTrainer(env, labelName, featuresName, weightsName, numLeaves,
                                                                      numTrees, minDatapointsInLeafs, learningRate, advancedSettings);

                if (onFit != null)
                {
                    return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
                }
                else
                {
                    return(trainer);
                }
            }, label, features, weights);

            return(rec.Output);
        }
        /// <summary>
        /// FastTree <see cref="RegressionContext"/> extension method.
        /// Predicts a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>.
        /// </summary>
        /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
        /// <param name="label">The label column.</param>
        /// <param name="features">The features colum.</param>
        /// <param name="weights">The optional weights column.</param>
        /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
        /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
        /// <param name="minDatapointsInLeafs">The minimal number of datapoints allowed in a leaf of a regression tree, out of the subsampled data.</param>
        /// <param name="learningRate">The learning rate.</param>
        /// <param name="advancedSettings">Algorithm advanced settings.</param>
        /// <param name="onFit">A delegate that is called every time the
        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
        /// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
        /// the linear model that was trained. Note that this action cannot change the result in any way;
        /// it is only a way for the caller to be informed about what was learnt.</param>
        /// <returns>The Score output column indicating the predicted value.</returns>
        public static Scalar <float> FastTree(this RegressionContext.RegressionTrainers ctx,
                                              Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
                                              int numLeaves            = Defaults.NumLeaves,
                                              int numTrees             = Defaults.NumTrees,
                                              int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs,
                                              double learningRate      = Defaults.LearningRates,
                                              Action <FastTreeRegressionTrainer.Arguments> advancedSettings = null,
                                              Action <FastTreeRegressionPredictor> onFit = null)
        {
            FastTreeStaticsUtils.CheckUserValues(label, features, weights, numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings, onFit);

            var rec = new TrainerEstimatorReconciler.Regression(
                (env, labelName, featuresName, weightsName) =>
            {
                var trainer = new FastTreeRegressionTrainer(env, labelName, featuresName, weightsName, numLeaves,
                                                            numTrees, minDatapointsInLeafs, learningRate, advancedSettings);
                if (onFit != null)
                {
                    return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
                }
                return(trainer);
            }, label, features, weights);

            return(rec.Score);
        }