Machine Learning - Trust By Code

Test your Machine learning knowledge

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1. What is Machine learning?

The selective acquisition of knowledge through the use of computer programs

The selective acquisition of knowledge through the use of manual programs

The autonomous acquisition of knowledge through the use of computer programs

The autonomous acquisition of knowledge through the use of manual programs

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2. K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?

Instance-based learning

Parametric learning

Non-parametric learning

Model-based learning

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3. Which of the following is not a supervised machine learning algorithm?

K-means

Naïve Bayes

SVM for classification problems

Decision tree

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4. An artificially intelligent car decreases its speed based on its distance from the car in front of it. Which algorithm is used?

Naïve-Bayes

Decision Tree

Linear Regression

Logistic Regression

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5. Which of the following statements is false about Ensemble learning?

It is a supervised learning algorithm

It is an unsupervised learning algorithm

More random algorithms can be used to produce a stronger ensemble

Ensembles can be shown to have more flexibility in the functions they can represent

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6.Which of the following statements is true about stochastic gradient descent?

It processes one training example per iteration

It is not preferred, if the number of training examples is large

It processes all the training examples for each iteration of gradient descent

It is computationally very expensive, if the number of training examples is large

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7. Decision tree uses the inductive learning machine learning approach.

False

True

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8. What elements describe the Candidate-Elimination algorithm?

depends on the dataset

just a set of instances

set of instances, set of candidate hypotheses

just a set of candidate hypotheses

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9.Which of the following statements is not true about boosting?

It mainly increases the bias and the variance

It tries to generate complementary base-learners by training the next learner on the mistakes of the previous learners

It is a technique for solving two-class classification problems

It uses the mechanism of increasing the weights of misclassified data in preceding classifiers

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10. What’s the key benefit of using deep learning for tasks like recognizing images?

They need less training data than other methods.

They’re easier to explain and understand than other models.

They can learn complex details from the data on their own.

They work faster and are more efficient computationally.

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11.Which algorithm is best suited for a binary classification problem?

K-nearest Neighbors

Decision Trees

Random Forest

Linear Regression

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12. Which type of machine learning algorithm falls under the category of “unsupervised learning”?

Linear Regression

K-means Clustering

Decision Trees

Random Forest

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