Weka, a popular suite of machine learning software written in Java, uses several algorithms for classification. Here are the algorithms mentioned in your question and their uses:

1. K-means: This is a clustering algorithm, not typically used for classification. It partitions the input data into K distinct clusters based on distance (Euclidean, Manhattan, etc.) measures.

2. Naive Bayes: This is a classification algorithm based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

3. Random Forest: This is also a classification algorithm. It operates by constructing multiple decision trees at training time and outputting the class that is the mode of the classes of the individual trees.

4. PCA (Principal Component Analysis): This is not a classification algorithm. It is a dimensionality reduction technique that is often used to simplify the dataset before applying a classification algorithm.

So, among the options given, Naive Bayes and Random Forest are used for classification in Weka.

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Weka, a popular suite of machine learning software written in Java, uses several algorithms for classification. Here are the algorithms mentioned in your question and their uses:

1. K-means: This is a clustering algorithm, not typically used for classification. It partitions the input data into K distinct clusters based on distance (Euclidean, Manhattan, etc.) measures.

2. Naive Bayes: This is a classification algorithm based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

3. Random Forest: This is also a classification algorithm. It operates by constructing multiple decision trees at training time and outputting the class that is the mode of the classes of the individual trees.

4. PCA (Principal Component Analysis): This is not a classification algorithm. It is a dimensionality reduction technique that is often used to simplify the dataset before applying a classification algorithm.

So, among the options given, Naive Bayes and Random Forest are used for classification in Weka.

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Weka, a popular suite of machine learning software written in Java, uses several algorithms for classification. Here are the algorithms mentioned in your question and their uses:

1. K-means: This is a clustering algorithm, not typically used for classification. It partitions the input data into K distinct clusters based on distance (Euclidean, Manhattan, etc.) measures.

2. Naive Bayes: This is a classification algorithm based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

3. Random Forest: This is also a classification algorithm. It operates by constructing multiple decision trees at training time and outputting the class that is the mode of the classes of the individual trees.

4. PCA (Principal Component Analysis): This is not a classification algorithm. It is a dimensionality reduction technique that is often used to simplify the dataset before applying a classification algorithm.

So, among the options given, Naive Bayes and Random Forest are used for classification in Weka.

Which algorithm is used for classification in Weka?K-meansNaive BayesRandom ForestPCA

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