UVa 103 Stacking Boxes

本文提供了一种解决 UVa103 Stacking Boxes 问题的高效算法实现,通过记忆化搜索(DP)求解最长递增子序列,并详细展示了邻接矩阵构造、边记录、结果计算等步骤。

### Stacking in Machine Learning Ensemble Methods Stacking, also known as stacked generalization, is a meta-algorithm that combines multiple classification or regression models via a meta-classifier/meta-regressor. Unlike bagging and boosting which focus on reducing variance and bias respectively, stacking aims at increasing the predictive power by leveraging predictions from different algorithms. In stacking, the training set is split into two parts. The first part trains each base learner while the second part forms what can be considered an out-of-sample dataset used to train the combiner model. This ensures that when generating features for the level-one model, there's no data leakage between training sets of individual learners and the overall stacker[^1]. The process involves using outputs from various classifiers/regressors as inputs/features for another classifier/regressor called the blender or meta-model. By doing this, stacking allows capturing patterns missed individually by constituent models leading potentially better performance than any single algorithm could achieve alone. ```python from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from mlens.ensemble import SuperLearner from sklearn.linear_model import LinearRegression from sklearn.svm import SVR import numpy as np X, y = make_regression(n_samples=1000, n_features=20, noise=0.5) X_train, X_test, y_train, y_test = train_test_split(X, y) layer_1 = SuperLearner() layer_1.add([LinearRegression(), SVR()]) layer_1.add_meta(LinearRegression()) layer_1.fit(X_train, y_train) predictions = layer_1.predict(X_test) print(f"Predictions shape: {np.shape(predictions)}") ```
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