Is it possible for a boosting algorithm to ensure zero training error?

Is it possible for a boosting algorithm to ensure zero training error?

2γ2 , AdaBoost ensures zero training error. small when N is large enough. Like all other machine learning algorithms, AdaBoost could also overfit the training set.

Is zero training error possible?

Zero training error is impossible in general, because of Bayes error (think: two points in your training data are identical except for the label).

Which of the following methods Cannot achieve zero training error on any linearly separable dataset?

Which of the following methods can achieve zero training error on any linearly separable dataset? Decision tree – Standard decision trees are having no learning biased. The training set error is always zero in decision trees if there is no label noise.

Which of the following models can achieve zero training error for classification?

Finding a logistic regression model which can achieve zero error on a training set training data for a binary classification problem with two features.

Is it possible to get zero training error?

The accepted answer is a bit complicated, so here’s a simpler answer: You can get zero training error by chance, with any model. Say your biased classifier always predicts zero, but your dataset happens to be all labeled zero.

How are training error and test error used in machine learning?

Training error vs test error There are two important concepts used in machine learning: the training error and the test error. Training Error: We get the by calculating the classification error of a model on the same data the model was trained on (just like the example above).

How do you boost a weak learning algorithm?

Informally, we “boost” the weak learning algorithm by feeding it examples from carefully constructed distributions, and then take a majority vote. This “reduction” from strong to weak learning is where all the magic happens. In this post we’ll get into the depths of this boosting technique.

How to validate a model with training data?

Table 2: A table with training data. We created a predictive model and applied it to the same data. This leads to a prediction for each row, stored in column p. Now we can easily compare how often our predictions are wrong.