Q1- What’s the trade-off between bias and variance?

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 too much complexity in the learning algorithm you’re using. This leads to the algorithm being highly sensitive to high degrees of variation in your training data, which can lead your model to overfit the data. You’ll be carrying too much noise from your training data for your model to be very useful for your test data.

The bias-variance decomposition essentially decomposes the learning error from any algorithm by adding the bias, the variance and a bit of irreducible error due to noise in the underlying dataset. Essentially, if you make the model more complex and add more variables, you’ll lose bias but gain some variance — in order to get the optimally reduced amount of error, you’ll have to tradeoff bias and variance. You don’t want either high bias or high variance in your model.

Q2- What is the difference between supervised and unsupervised machine learning?

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.

Q3- How is KNN different from k-means clustering?

K-Nearest Neighbors (KNN) 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 requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.
The critical difference here is that KNN needs labeled points and is thus supervised learning, while k-means doesn’t — and is thus unsupervised learning.

Q4- Define precision and recall.

Recall is also known as the true positive rate: the amount of positives your model claims compared to the actual number of positives there are throughout the data.

Precision is also known as the positive predictive value, and it is a measure of the amount of accurate positives your model claims compared to the number of positives it actually claims.

It can be easier to think of recall and precision in the context of a case where you’ve predicted that there were 10 apples and 5 oranges in a case of 10 apples. You’d have perfect recall (there are actually 10 apples, and you predicted there would be 10) but 66.7% precision because out of the 15 events you predicted, only 10 (the apples) are correct.

Q5- What is Bayes’ Theorem? How is it useful in a machine learning context?

Bayes’ Theorem gives you the posterior probability of an event given what is known as prior knowledge.

Mathematically, it’s expressed as the true positive rate of a condition sample divided by the sum of the false positive rate of the population and the true positive rate of a condition. Say you had a 60% chance of actually having the flu after a flu test, but out of people who had the flu, the test will be false 50% of the time, and the overall population only has a 5% chance of having the flu. Would you actually have a 60% chance of having the flu after having a positive test?

Bayes’ Theorem says no. It says that you have a (.6 * 0.05) (True Positive Rate of a Condition Sample) / (.6*0.05)(True Positive Rate of a Condition Sample) + (.5*0.95) (False Positive Rate of a Population) = 0.0594 or 5.94% chance of getting a flu.

Bayes’ Theorem is the basis behind a branch of machine learning that most notably includes the Naive Bayes classifier.

Q6- Why is “Naive” Bayes naive?

Despite its practical applications, especially in text mining, Naive Bayes is considered “Naive” because it makes an assumption that is virtually impossible to see in real-life data: the conditional probability is calculated as the pure product of the individual probabilities of components. This implies the absolute independence of features — a condition probably never met in real life.

A Naive Bayes classifier that figured out that you liked pickles and ice cream would probably naively recommend you a pickle ice cream.

Q7- Explain the difference between L1 and L2 regularization.

L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacian prior on the terms, while L2 corresponds to a Gaussian prior.

Q8- What’s the difference between Type I and Type II error?

Type I error is a false positive, while Type II error is a false negative.

Briefly stated, Type I error means claiming something has happened when it hasn’t, while Type II error means that you claim nothing is happening when in fact something is.

A clever way to think about this is to think of Type I error as telling a man he is pregnant, while Type II error means you tell a pregnant woman she isn’t carrying a baby.

Q9- What’s the difference between probability and likelihood?

In layman terms, probability is what is normalized to sum up to one, naturally by itself, or through the way the model has been constructed. Probability defines a distribution.

Likelihood can be used to gauge how likely an event is, and compare which of two events is more likely. But it's not a probability, and it does not define a probability distribution.

For an in detail explanation refer to this answer on stats.stackexchange

Q10- What is deep learning, and how does it contrast with other machine learning algorithms?

Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.

Q11- What’s the difference between a generative and discriminative model?

A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

In General, A Discriminative model ‌models the decision boundary between the classes. A Generative Model ‌explicitly models the actual distribution of each class. In final both of them is predicting the conditional probability . But Both models learn different probabilities.

A Generative Model ‌learns the joint probability distribution p(x,y). A Discriminative model ‌learns the conditional probability distribution p(y|x).

Examples of Generative model are :- Naive Bayes and Hidden Markov Models. And examples of Discriminative model are :- Logistic Regression and Support Vector Machines.

Q12- How is a decision tree pruned?

Pruning is what happens in decision trees when branches that have weak predictive power are removed in order to reduce the complexity of the model and increase the predictive accuracy of a decision tree model. Pruning can happen bottom-up and top-down, with approaches such as reduced error pruning and cost complexity pruning.

Reduced error pruning is perhaps the simplest version. If a node doesn’t decrease predictive accuracy, keep it pruned meaning remove the subtree of that node and make it a leaf. While simple, this heuristic actually comes pretty close to an approach that would optimize for maximum accuracy.

Q13- What cross-validation technique would you use on a time series dataset?

Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data — it is inherently ordered by chronological order. If a pattern emerges in later time periods for example, your model may still pick up on it even if that effect doesn’t hold in earlier years!

You’ll want to do something like forward chaining where you’ll be able to model on past data then look at forward-facing data.
• fold 1 : training [1], test [2]
• fold 2 : training [1 2], test [3]
• fold 3 : training [1 2 3], test [4]
• fold 4 : training [1 2 3 4], test [5]
• fold 5 : training [1 2 3 4 5], test [6]

Q14- What’s the F1 score? How would you use it?

The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much.

Q15- How would you handle an imbalanced dataset?

An imbalanced dataset is when you have, for example, a classification test and 90% of the data is in one class. That leads to problems: an accuracy of 90% can be skewed if you have no predictive power on the other category of data! Here are a few tactics to get over the hump:
1- Collect more data to even the imbalances in the dataset.
2- Resample the dataset to correct for imbalances.
3- Try a different algorithm altogether on your dataset.

Q16- How do you ensure you’re not overfitting with a model?

There are three main methods to avoid overfitting:
1- Keep the model simpler: reduce variance by taking into account fewer variables and parameters, thereby removing some of the noise in the training data.
2- Use cross-validation techniques such as k-folds cross-validation.
3- Use regularization techniques such as LASSO that penalize certain model parameters if they’re likely to cause overfitting.

Q17- What’s the “kernel trick” and how is it useful?

The Kernel trick involves kernel functions that can enable in higher dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space.

It allows us to operate in the original feature space without computing the coordinates of the data in a higher dimensional space.

This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates. Many algorithms can be expressed in terms of inner products. Using the kernel trick enables us effectively run algorithms in a high-dimensional space with lower-dimensional data.

However, one critical thing to keep in mind is that when we map data to a higher dimension, there are chances that we may overfit the model. Thus choosing the right kernel function (including the right parameters) and regularization are of great importance.

Q18- How do you handle missing or corrupted data in a dataset?

You could find missing/corrupted data in a dataset and either drop those rows or columns, or decide to replace them with another value.

In Pandas, there are two very useful methods: isnull() and dropna() that will help you find columns of data with missing or corrupted data and drop those values. If you want to fill the invalid values with a placeholder value (for example, 0), you could use the fillna() method.

Q19- What evaluation approaches would you work to gauge the effectiveness of a machine learning model?

You would first split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data. You should then implement a choice selection of performance metrics. You could use measures such as the F1 score, the accuracy, and the confusion matrix.

Q20- Explain how a ROC curve works.

The ROC (receiver operating characteristic) curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).