Which of the following classifiers can perfectly classify the following data?

Which of the following classifiers can perfectly classify the following data? Decision Tree only. Decision trees of depth 2 which first splits on X and then on Y will perfectly classify it.

How would you arrange the dataset for your learning algorithm training cross validation and testing?

The best approach is to select/arrange data randomly. Basically you have three data sets: training, validation and testing. You train the classifier using ‘training set’, tune the parameters using ‘validation set’ and then test the performance of your classifier on unseen ‘test set’.

Is KNN a parametric algorithm and if so what are its implications?

KNN is a non-parametric and lazy learning algorithm. Non-parametric means there is no assumption for underlying data distribution. All training data used in the testing phase. This makes training faster and the testing phase slower and costlier.

What is classification method?

Classification methods aim at identifying the category of a new observation among a set of categories on the basis of a labeled training set. Depending on the task, anatomical structure, tissue preparation, and features the classification accuracy varies.

What are the different types of classifiers?

Different types of classifiers

• Perceptron.
• Naive Bayes.
• Decision Tree.
• Logistic Regression.
• K-Nearest Neighbor.
• Artificial Neural Networks/Deep Learning.
• Support Vector Machine.

Which of the following options are true for K fold cross validation?

22) Which of the following options is/are true for K-fold cross-validation? Increase in K will result in higher time required to cross validate the result. Higher values of K will result in higher confidence on the cross-validation result as compared to lower value of K.

What does K in K-nearest neighbors classification mean?

It takes a bunch of unlabeled points and tries to group them into “k” number of clusters. It is unsupervised because the points have no external classification. The “k” in k-means denotes the number of clusters you want to have in the end.

What is k-nearest neighbor classifier in machine learning?

The algorithm for the k-nearest neighbor classifier is among the simplest of all machine learning algorithms. k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all the computations are performed, when we do the actual classification.

How is k nearest algorithm implemented in classification problem?

In a classification problem, k nearest algorithm is implemented using the following steps. Pick a value for k, where k is the number of training examples in feature space. Calculate the distance of unknown data points from all the training examples.

What is neighneighbors-based classification?

Neighbors-based methods are known as non-generalizing machine learning methods, since they simply “remember” all of its training data. Classification can be computed by a majority vote of the nearest neighbors of the unknown sample. Now let’s get a little bit more mathematically:

What is the best distance to use for classification?

The distance can, in general, be any metric measure: standard Euclidean distance is the most common choice. Neighbors-based methods are known as non-generalizing machine learning methods, since they simply “remember” all of its training data. Classification can be computed by a majority vote of the nearest neighbors of the unknown sample.