- stacking machine learning
2. data scrubbing happens before data being written into SSD
3. lazy learning-
Lazy learning methods simply store the data and generalizing beyond these data is postponed until an explicit request is made. … Eager learning methods use the same approximation to the target function, which must be learned based on training examples and before input queries are observed.
variance and bias — A model that exhibits small variance and high bias will underfit the target, while a model with high variance and little bias will overfit the target. A model with high variance may represent the data set accurately but could lead to overfitting to noisy or otherwise unrepresentative training data.
4. Scikit-learn, TensorFlow, Pytorch difference
5. bagging and boosting
https://towardsdatascience.com/decision-tree-ensembles-bagging-and-boosting-266a8ba60fd9
