Most Asked Machine Learning Interview Questions

K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering … Read more

Supervised learning requires training labeled data. For example, in order to do classification (a supervised learning task), you’ll need to first label the data you’ll use to train the model to classify data into your labeled groups. Unsupervised learning, in contrast, does not require labeling data explicitly. When asked about your favorite algorithm in a … Read more

Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm you’re using. This can lead to the model underfitting your data, making it hard for it to have high predictive accuracy and for you to generalize your knowledge from the training set to the test set. Variance is error due to … Read more

You can reduce dimensionality by combining features with feature engineering, removing collinear features, or using algorithmic dimensionality reduction. Now that you have gone through these machine learning interview questions, you must have got an idea of your strengths and weaknesses in this domain. Reducing dimensionality is a crucial aspect of machine learning, especially when dealing … Read more

Kernel SVM is the abbreviated version of the kernel support vector machine. Kernel methods are a class of algorithms for pattern analysis, and the most common one is the kernel SVM. Kernel SVM, or Kernel Support Vector Machine, is a powerful extension of the traditional Support Vector Machine (SVM) algorithm used for classification and regression … Read more