In this section we will start off by looking at some well known algorithms supported by Weka. What we will learn in this post applies to the machine learning algorithms used across the Weka platform, but the Explorer is the best place to learn more about the algorithms as they are all ava...
You can also access this dataset in your Weka installation, under thedata/directory in the file calleddiabetes.arff. Weka Load Diabetes Dataset About Data Filters in Weka Weka provides filters for transforming your dataset. The best way to see what filters are supported and to play with them o...
All machine learning models use certain assumptions to make predictions. Linear regression, logistic regression, decision tree, random forest, KNN (K-Nearest Neighbors), support vector machines (SVM), and all other ML models are based on some fundamental axioms or assumptions about the nature and ...
You can still start with an easy one such as L2-regularized Logistic Regression, or k-means, but you should also push yourself to implement more interesting ones such as LDA (Latent Dirichlet Allocation) or SVMs. You can use a reference implementation in one of the many existing libraries to...
We are going to use two models as submodels for stacking and a linear model as the aggregator model. This part is divided into 3 sections: Sub-model #1: k-Nearest Neighbors. Sub-model #2: Perceptron. Aggregator Model: Logistic Regression. Each model will be described in terms of the fun...
Weka Configuration for the AdaBoost Algorithm The weak learner within the AdaBoost model can be specified by the classifier parameter. The default is the decision stump algorithm, but other algorithms can be used. a key parameter in addition to the weak learner is the number of models to creat...
It is perhaps the simplest algorithm to implement. It requires that you store all of the distinct outcome values in the training data, which could be large on regression problems with lots of distinct values. Becauserandom numbersare used to make decisions, it is a good idea to fix the rand...
The Keras metrics API is limited and you may want to calculate metrics such as precision, recall, F1, and more. One approach to calculating new metrics is to implement them yourself in the Keras API and have Keras calculate them for you during model training and during model evaluation....
There’s good reason to be confused. It is confusing to beginners. Machine learning is a large field of study, and not all much of it is going to be relevant to you if you’re focused on solving a problem. In this post, I hope to clear things up for you. ...
I really learned a lot from your post and applied it to my academic project. However, there are few questions still in my mind. In our project, we want to compare different machine algorithms with and without 10-fold cv, including logistics regression, SVM, random forest, and ANN. We can...