摘要: Reinforcement learning is a type of machine learning in whicha computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment. This learning approach enables the computer to make a series of decisions that maximize a reward metric for the task without human intervention and without being explicitly programmed to achieve the task.

摘要: 堆疊是集成多個分類法或回歸模型的方式。有很多方法可以集成模型,眾所周知的模型有Bagging或Boosting。Bagging允許多個具有高方差類似的分類模型中取平均以減少差異。Boosting建立多個增量模型,以減少誤差,同時保持方差小。

摘要: You probably used random forest for regression and classification before, but time series forecasting? Hold up you’re going to say; time series data is special! And you’re right. When it comes to data that has a time dimension, applying machine learning (ML) methods becomes a little tricky.....

摘要: Imbalanced classes are a common problem in machine learning classification where there are a disproportionate ratio of observations in each class. Class imbalance can be found in many different areas including medical diagnosis, spam filtering, and wildfire detection.

摘要: 粒子群算法(Particle swarm optimization,PSO)是模擬群體智能所建立起來的一種優化算法,主要用於解決最優化問題(optimization problems)。 1995年由 Eberhart和Kennedy 提出,是基於對鳥群覓食行為的研究和模擬而來的。

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