Backward propagation is the process of adjusting the weights and biases in the network based on the prediction error. It involves calculating the gradients of the loss function and adjusting the parameters to minimize the error.

Backward stepwise is the method in which the model starts with all predictor variables, and at each step, the variable that contributes the least to the model’s performance is removed until no further improvement is achieved or a predefined stopping criterion is met.

Bagging is an ensemble method that involves training multiple base learners independently on different random subsets of the training data (sampling with replacement). The final prediction is typically an average or voting over the predictions of all the base learners.

A bar chart is a graphical representation of categorical data that uses rectangular bars of varying lengths to visualize the frequency or count of each category.

Base learners are the individual models that constitute the ensemble. Base learners can be any machine learning algorithm, such as decision trees, support vector machines, neural networks, etc.

Bayes’ theorem is a fundamental equation used in Bayesian networks to update probabilities based on new evidence. It calculates the posterior probability of an event given prior knowledge and observed evidence.

Bayesian network, also known as a Bayesian belief network or probabilistic graphical model, is a powerful and popular method for representing and reasoning about uncertainty and probabilistic relationships between variables. It is a graphical model that represents a set of variables and their conditional dependencies through a directed acyclic graph.

A bias term is added to the weighted sum of inputs in each neuron. It allows the network to learn a bias towards certain values and improves the accuracy of the model.

Boosting is an iterative ensemble method where base learners are trained sequentially, and each learner focuses on correcting the mistakes made by its predecessors. Boosting assigns higher weights to misclassified instances, effectively giving more attention to challenging samples.

A box plot (box-and-whisker plot) is a graphical summary of the distribution of numerical data that displays the median, quartiles, and potential outliers, providing a concise view of the data’s central tendency and spread.

A branch or a sub-tree is a sub-section of a decision tree.