Accenture Machine Learning Interview Questions

Accenture – Machine Learning Interview Questions

Here is the list Machine Learning Interview Questions which are recently asked in Accenture company. These questions are included for both Freshers and Experienced professionals.

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1. What’s the trade-off between bias and variance?

The bias-variance tradeoff refers to a decomposition of the prediction error in the machine learning as the sum of a bias and a variance term. An example of the bias-variance tradeoff in practice.

2. What is the difference between supervised and unsupervised machine learning?

Supervised learning algorithms are trained the using labeled data. Unsupervised learning algorithms are trained using unlabeled data. supervised learning, input data is provided to the model along with the output. unsupervised learning, only input data is provided to the model.

3. How is KNN different from k-means clustering?

KNN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters.

4. Explain how a ROC curve works.

A ROC curve is constructed by plotting the true positive rate against the false positive rate. A discrete classifier that the returns only the predicted class gives a single point on the ROC space.

5. What is Bayes’ Theorem? How is it useful in a machine learning context?

Bayes Theorem is a useful tool in applied machine learning. It provides a way of thinking about the relationship between the data and a model. A machine learning algorithm or model is a specific way of thinking about the structured relationships in the data.

6. Why is “Naive” Bayes naive?

Naive Bayes is called the naive because it assumes that the each input variable is independent. This is a strong assumption and unrealistic for real data; however,that the technique is very effective on a large range of complex problems.

7. Explain the difference between L1 and L2 regularization

The main intuitive the difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.

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8. What’s your favorite algorithm, and can you explain it to me in less than a minute?

Hands down logistic regression (with many bells and whistles like stochastic gradient descent, feature hashing and penalties).

9. What’s the difference between Type I and Type II error?

Type 1 error, in statistical hypothesis testing, is the error caused by rejecting a null hypothesis when it is true. Type II error is the error that occurs when the null hypothesis is accepted when it is not the true. Type I error is equivalent to false positive. Type II error is equivalent to a false negative.

10. What’s the difference between probability and likelihood?

Probability is used to finding the chance of occurrence of a particular situation, whereas Likelihood is used to the generally the maximizing the chances of a particular situation to occur.

11. What is deep learning, and how does it contrast with other machine learning algorithms?

Deep learning algorithms try to learn high-level features from data. This is a very distinctive part of Deep Learning and a major step ahead of traditional Machine Learning. Therefore, deep learning reduces the task of developing new feature extractor for the every problem.

12. What cross-validation technique would you use on a time series dataset?

Cross Validation technique on a time series dataset are:

• Split randomly data in train and test set.
• Focus on train set and split it again randomly in chunks (called folds).
• Let's say you got 10 folds; train on 9 of them and test on the 10th.
• Repeat step three 10 times to get 10 accuracy measures on 10 different and separate folds.

13. How is a decision tree pruned?

Post-pruning is the most common way of simplifying the trees. Here, nodes and subtrees are replaced with the leaves to improve complexity. Pruning can not only significantly reduce the size but also improve the classification accuracy of unseen objects.

14. Which is more important to you– model accuracy, or model performance?

The accuracy of the model and performance of the model are directly proportional and hence better the performance of the model, more accurate are the predictions.

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