Fundamentals and Methods of Machine and Deep Learning. Pradeep Singh

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Fundamentals and Methods of Machine and Deep Learning - Pradeep Singh


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they share information about sending or receiving the information about the training data [21, 22].

hyperparameters
. Based on the values of the prior posterior probability distribution of random variables with observed label classes, the independence posterior density id computed as follows:

      The inferences drawn are based on the unknown random variables, i.e., P, π, t, V, and α which are collected using Gibbs and rejection sampling methodology. A high-level representation of BCC is shown in Figure 2.4. First parameters of BCC model, hyperparameters, and posterior probabilities are summed to generate final prediction as output.

      Figure 2.4 A high-level representation of Bayesian classifier combination (BCC).

      Figure 2.5 A high-level representation of bucket of models.

      One of the best suitable approaches for cross-validation among multiple models in ensemble learning is bake off contest, the pseudo-code of which is given below.

      Pseudo-code: Bucket of models

       For each of the ensemble model present in the bucket doRepeat constant number of timesDivide the training set into parts, i.e., training set and test set randomlyTrain the ensemble model with training setTest the ensemble model with test setChoose the ensemble model that yields maximum average score value

      Some of the advantages offered by bucket of models in diagnosing the zonotic diseases are as follows: high quality prediction, provides unified view of the data, negotiation of local patterns, less sensitive to outliers, stability of the model is high, slower model gets benefited from faster models, parallelized automation of tasks, learning rate is good on large data samples, payload functionality will be hidden from end users, robustness of the model is high, error generation rate is less, able to handle the random fluctuations in the input data samples, length of the bucket is kept medium, easier extraction of features from large data samples, prediction happens by extracting the data from deep web, linear weighted average model is used, tendency of forming suboptimal solutions is blocked, and so on [25, 26].

      The generalization approach in stacking splits the existing data into two parts one is training dataset and another is testing dataset. The base model is divided into K-NN base models, the base model will be fitted into K–1 parts which leads to the prediction of the Kth part. The base model will further fit into the whole training dataset to compute the performance over the testing samples. The process gets repeated on the other base models which include support vector machine, decision tree, and neural network to make predictions over the test models [29]. A high-level representation of stacking is shown in Figure 2.6. Multiple models are considered in parallel, and training data is fed as input to each of the model. Every model generated the predictions and summation of each of the predictions is fed as input to generalizer. Finally, generalizer generates final predictions based on the summation of the predictions generated by each of the model.


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