Latest Machine Learning Interview Questions and Answers
What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?
The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a single model. Bagging is a method in ensemble for improving unstable estimation or classification schemes. While boosting method are used sequentially to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.
What is bias-variance decomposition of classification error in ensemble method?
The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.
What is an Incremental Learning algorithm in ensemble?
Incremental learning method is the ability of an algorithm to learn from new data that may be available after classifier has already been generated from already available dataset.
What is PCA, KPCA and ICA used for?
PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.
What is dimension reduction in Machine Learning?
In Machine Learning and statistics, dimension reduction is the process of reducing the number of random variables under considerations and can be divided into feature selection and feature extraction
What are support vector machines?
Support vector machines are supervised learning algorithms used for classification and regression analysis.
What are the components of relational evaluation techniques?
The important components of relational evaluation techniques are
a) Data Acquisition
b) Ground Truth Acquisition
c) Cross Validation Technique
d) Query Type
e) Scoring Metric
f) Significance Test
What are the different methods for Sequential Supervised Learning?
The different methods to solve Sequential Supervised Learning problems are
a) Sliding-window methods
b) Recurrent sliding windows
c) Hidden Markow models
d) Maximum entropy Markow models
e) Conditional random fields
f) Graph transformer networks
What are the areas in robotics and information processing where sequential prediction problem arises?
The areas in robotics and information processing where sequential prediction problem arises are
a) Imitation Learning
b) Structured prediction
c) Model based reinforcement learning
What is batch statistical learning?
Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.
What is PAC Learning?
PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.
What are the different categories you can categorized the sequence learning process?
a) Sequence prediction
b) Sequence generation
c) Sequence recognition
d) Sequential decision
What is sequence learning?
Sequence learning is a method of teaching and learning in a logical manner.
What are two techniques of Machine Learning ?
The two techniques of Machine Learning are
a) Genetic Programming
b) Inductive Learning
Give a popular application of machine learning that you see on day to day basis?
The recommendation engine implemented by major ecommerce websites uses Machine Learning