What is feature selection in Weka?

Learner Based Feature Selection A popular feature selection technique is to use a generic but powerful learning algorithm and evaluate the performance of the algorithm on the dataset with different subsets of attributes selected. The subset that results in the best performance is taken as the selected subset.

What is kNN algorithm in Weka?

Tuning k-Nearest Neighbour In this experiment we are interested in tuning the k-nearest neighbor algorithm (kNN) on the dataset. In Weka this algorithm is called IBk (Instance Based Learner). The IBk algorithm does not build a model, instead it generates a prediction for a test instance just-in-time.

What is the J48 algorithm?

J48 Classifier. It is an algorithm to generate a decision tree that is generated by C4. 5 (an extension of ID3). It is also known as a statistical classifier. For decision tree classification, we need a database.

What is correlation based feature selection?

Correlation is a well-known similarity measures between two features. If two features are linearly dependent, then their correlation coefficient is ±1. If the features are uncorrelated, the correlation coefficient is 0. If the value is higher than the threshold value (say 0.5), then the feature will be selected.

Where will be the preprocessing algorithm available in Weka?

weka→filters→supervised→attribute→AttributeSelection After you are satisfied with the preprocessing of your data, save the data by clicking the Save button. You will use this saved file for model building. In the next chapter, we will explore the model building using several predefined ML algorithms.

Which of the following algorithm can be used for variable selection?

9) Which of the following algorithms do we use for Variable Selection? In case of lasso we apply a absolute penality, after increasing the penality in lasso some of the coefficient of variables may become zero. where g(z) is the logistic function.

Which of the following is a feature selection method?

Some of the most popular examples of these methods are LASSO and RIDGE regression which have inbuilt penalization functions to reduce overfitting. Lasso regression performs L1 regularization which adds penalty equivalent to absolute value of the magnitude of coefficients.

How do I tune my KNN model?

Using Hyperparameters Tuning can improve model performance by about 20\% to a range of 77\% for all evaluation matrices….Which is More Promising: Data Science or Software Engineering? | Data Driven Investor

1. Determine the value of K.
2. Calculate the distance of new data with training data.

What is ZeroR in Weka?

ZeroR is the simplest classification method which relies on the target and ignores all predictors. ZeroR classifier simply predicts the majority category (class). Although there is no predictability power in ZeroR, it is useful for determining a baseline performance as a benchmark for other classification methods.

Is J48 and C4 5 the same?

J48 is an open source Java implementation of the C4. 5 algorithm in the Weka data mining tool.