Data Science Trivia — 213 Data Science Interview Questions

Machine Learning

Name three supervised learning algorithms.

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Name three supervised learning algorithms.
Name three unsupervised learning algorithms.
What is the type of machine learning algorithms used to draw inferences from labeled data?
What is the type of machine learning algorithms used to draw inferences from data without labels?
Graphical representation of the contrast between true positives and false positives rates at various thresholds.
Name three types of kernels in SVM.
What is pruning in a decision tree?
What is ensemble learning?
Would you use k-fold cross validation on time series data? Explain.
What is the process of adding tuning parameters to a model to induce smoothness in order to prevent overfitting?
What is count encoding?
What is one hot encoding?
What is target encoding?
What is data leakage?
Imagine you are applying a decision tree based model to a numerical dataset and you run a experiment where you scale the features. What model would give the best results the original or the scaleted one?
What is the difference between L1 (Lasso) and L2 (Ridge) regularization?
What is a confusion matrix?
Confusion Matrix should be used for supervised or unsupervised learning problems?
What is boosting when referring to machine learning algorithms?
Explain what is bucketing.
How many centroids a k-means algorithm with k = 3 would find?
What is the difference between a dense and a sparse feature?
What is downsampling in the context of class-imbalanced dataset?
What is early stopping?
Logistic regression is useful for estimating continuous variables?
Creating a model that matches the training data so closely that the model fails to make correct predictions on new data.
Explain the basic concept of random forest.
What does random means in the random forest term?
If someone ask you: Out of all the possible positive labels, how many did the model correctly identify? Which metric should you calculate?
Producing a model with poor predictive ability because the model hasn't captured the complexity of the training data.
What is precision?
What is recall?
What is F1-score?
What is better to have a F1-score equals to 1 or to 0?
Which regularization technique penalizes the sum of absolute value of weights? L1 or L2?
Which regularization technique penalizes the sum of square weights? L1 or L2?
L1 regularization is also called?
L2 regularization is also called?
Can we have both L1 and L2 regularization components in a linear model?
What is Elastic net regularization?
What is unsupervised learning?
Does feature selection tends to increase overfitting?
Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, R-squared are metrics for evaluation which type of models?
Model simplification tends to decrease the bias and increase the variance which leads to underfitting. True or False?
Model complexity tends to decrease the bias and increase the variance which leads to overfitting. True or False?
Accuracy, Precision, Recall, F1 score, ... are metrics to evaluate which types of models?
Why is naive Bayes 'naive'?
Is KNN a clustering algorithm?
How is random forest different from gradient boosting algorithm?
It is possible to design a Linear regression algorithm using a neural network?
Which regularization would you use if your model is underfitting?
Standardization of features is required before training a Logistic Regression?
What is the difference between online and batch Learning?
Bagging stands for?
What is the difference between boosting and bagging?
High bias can cause an algorithm to underfit? True or False?
High variance can cause an algorithm to overfit? True of False?
What is variance in the context of Machine Learning?
Explain what is Bias-Variance tradeoff?
What happens to our linear regression if we have columns x, y, z. And z is a sum of x and y?
Is logistic regression a linear model? Why?
What is TF-IDF?
What is overfitting?
What is the precision-recall curve?
Can we use L1 regularization for feature selection?
What is bag of words?
What is clustering?
What is a time series?
When building your vocabulary set, it’s ok to use the tokens from the entire dataset to reduce the size of your out-of-vocab list?
What is the main data structure used in causal modelling?
What is the generalization of the median?
What is the difference between "long" and "wide" format data?
Technique used to sample based on a probability where each sample unit is a collection of a cluster element.
What is a Type I and a Type II error?
What two parameters defined a normal distribution?
In a normal distribution which parameter indicates where the bell is centered?
In a normal distribution which parameter indicates how wide is the bell?
What is the difference of and nominal and ordinal feature?
What does high and low cardinality mean?
What is R-squared?
What is a residual?
What is dimensionality reduction?
What is a false negative?
What is a false positive?
Values distant from most other values are?
Precision is the rate between?
What does stationarity in a dataset means?
The normal distribution derives its importance from which famous theorem?
What does MSE mean?
What does RMSE mean?
Name three techniques of dimensionality reduction.
Measures how far a set of (random) numbers are spread out from their average value.
What values could you infer from a boxplot?
Is rotation necessary in PCA?
What is the difference between covariance and correlation?
Is it possible capture the correlation between continuous and categorical variable?
If pearson correlation is 0 between two variables can we assume that there isn't any relation between them?
Does mean imputation takes into account feature correlation?
Give 3 techniques for handling missing values.
What is the Law of Large Numbers?
What is Survivorship bias?
What is a confounding variable?
How Interquartile Range is calculated?
What is autocorrelation?
What is MSE?
What is RMSE?
How can we select K for K-means?
What is precision and recall at k?
Who is knows as the creator of the GANs architecture?
LeCun, Cortes, and Burges compiled a very famous dataset containing 60000 images of handwritten digits. What is the name of this dataset?
The least squares method was published by a famous french mathematician called.
In 1812 Laplace published "Théorie Analytique des Probabilités" in which he defines a famous theorem. Which theorem was it?
Andrey Markov first describes techniques he used to analyse a poem. Later this techniques become known as Markov chains. True or False.
In 1951 Marvin Minsky and Dean Edmonds conceived what is known as the firts neural network machine. What is it's name?
Who invented the perceptron?
In 1970 Seppo Linnainmaa published the general method for automatic differentiation. This corresponds to the modern version of:
In which decade does Tin Kam Ho published a paper describing random decision forests?
In 1995 Corinna Cortes and Vladimir Vapnik publish their work explaining a new supervised learning algorithm called:
What was the name of the first computer that beat against the the world champion at chess?
Who invented the LSTM architecture in deep learning?
When was Torch released?
Who envisioned and created the ImageNet dataset?
What is the name of the computer that beat a professional player at Go?
Which computer scientists received the Turing Award for their contributions in deep learning?
When was the nearest neighbor algorithm was created?
When was the Turing Test created?
How coined the term deep learning in 2006?
When was TensorFlow launched?
How is the creator of Keras?
PyTorch was primarily developed by which company?
The R language was designed by statisticians?
Who are credited by the creation of R?
In 1989 Christopher Watkins published his PhD thesis “Learning from Delayed Rewards” where he introduced the concept of:
Who is known of the founding father of convolutional nets?
Who are known as the "Good Parents of AI"?
Was Bayes's Theorem published after his death?
What object did Yann Lecunn use in NIPS 2016 to describe the ’layers’ of intelligence?
What defunct deep learning library was published in 2007 by Montreal Institute for Learning Algorithms?
What language was the original AlexNet (ILSVRC 2012) written in?
What was the final score of the 2016 match between Google DeepMind’s AlphaGo and Go grandmaster Lee Sedol?
This person’s notable students include Ruslan Salakhutdinov, Ilya Sutskever, and Yann LeCun?
What was the dimension of all the embedding and sublayers of the original Transformer (Attention is All You Need 2017)?
In 2016 Google’s AlphaGo won four out of five games of Go against Lee Sedol, a professional Go player from South Korea (9 dan rank). What hardware powered the AlphaGo software to victory?
Type of backpropagation where we use only a single training example for calculation of the gradient and update parameters.
Type of backpropagation where we use the whole dataset to perform a single step.
Type of backpropagation where we use a fixed number of training examples which is lees than the actual dataset to perform a calculation of the gradient
Name three different deep learning frameworks.
What is AdaGrad?
Which ones are two actors in a convolutional operation?
How does dropout regularization works in deep learning?
What is an epoch?
What is a batch?
Which are the two components of a Generative Adversarial Network?
What is the function of the generator in a GAN?
What is the function of the discriminator in a GAN?
What does gradient descent tries to minimize?
Which activation function follows these rules: If input is negative or zero, output is 0 and if input is positive, output is equal to input.
Explain what a softmax function does?
What is learning rate?
What does LSTM means?
Type of layer that tipically has a cell, input gate, output gate and a forget gate?
Sigmoid squishes values to be between?
What does GRU means?
Tanh function squishes values to be between?
Type of layer that tipically has a reset gate and a output gate?
What if we set all the weights of a neural network to 0?
What happens when the learning rate is too large?
What happens when the learning rate is too small?
Why do we need activation functions?
What are the problems with sigmoid as an activation function?
What is ReLU? How is it better than sigmoid?
What’s pooling in CNN? Why do we need it?
Why did the output dimension of all the embedding and sublayers of original Transformer (Attention is All You Need 2017) need to be the same?
One hot encoding is an excellent solution to transform categorical features with high cardinality.
The F1 Score is a metric defined as the harmonic mean of precision, recall, and accuracy.
As the number of hidden layers increases in a neural network, its capacity also increases.
Initializing a neural network’s weights with zero is our best bet to allow the network to converge.
As the dropout ratio used in a neural network increases, the network’s capacity also increases.
Stochastic Gradient Descent is the proper technique to use when the full dataset doesn’t fit in memory.
Batch Normalization is an efficient backpropagation technique that allows neural networks to learn.
Gradient Descent is an algorithm used to minimize overfitting in neural networks.
Having high bias means the model is too simple and can’t capture many features during the training phase. This is also known as overfitting.
Precision is a performance metric that defines a classification models ability to identify only relevant samples.
Recall is a performance metric that defines a classification models ability to identify all relevant samples.
Softmax is an activation function that always returns the input value if it’s positive and zeroes otherwise.
In neural networks, an activation function’s most essential role is to decide whether a unit or neuron should fire.
Using a learning rate that’s too low will cause the training process to be very slow.
ReLU is an activation function that, given an input vector, generates an output where the sum of the values in the vector is equal to one.
An autoencoder is a neural network that automatically learns by using its inputs as the expected output.
Convolutional neural networks are translation invariant.
Autoencoders are one of the most popular supervised learning methods.
The type of problem where you need to classify the input into a single class is called multi class classification.
Dropout is an excellent regularization technique that speeds the training process.
A neural network with a single layer is capable of approximating any function.
Leaky ReLU is an activation function that allows the passing of small sized negative values if the network’s input value is less than zero.
Bagging is the concept of splitting a dataset and randomly placing it into bags for training a model.
A Categorical feature is a feature that can only take a limited number of possible values.
What do temperature, top-p and top-k control?
When do you use RAG instead of fine-tuning?
What are the tradeoffs in RAG chunk size?
Why combine embeddings with keyword search?
How do you evaluate a RAG system?
What actually reduces hallucinations?
Why is a bigger context window not always the answer?
Prompt, system prompt, tools: what does each do?
Why did transformers replace RNNs for most NLP tasks?
How does BERT differ from a GPT-style model?
When should you build a workflow instead of an agent?
What is an embedding?
When do few-shot examples beat zero-shot prompting?
What does RLHF change about a base model?
How does byte pair encoding tokenization work?
What is the KV cache, and why does it cap concurrency?

Data Science Trivia — 213 data science interview questions and answers

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