### 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.

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 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.

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- Explain how a ROC curve works.

The
ROC curve is a graphical repsentation 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).

### Q5- Define pcision 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. pcision is also known as the positive pdictive
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 pcision in the context of a case
where you’ve pdicted 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 pdicted there would be 10) but 66.7% pcision because out of the
15 events you pdicted, only 10 (the apples) are correct.

### Q6- 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 expssed 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.

Mathematically, it’s expssed 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. That’s something important
to consider when you’re faced with machine learning interview questions.

### Q7- 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.

As a Quora commenter put it whimsically, a Naive Bayes classifier that figured out that you liked pickles and ice cream would probably naively recommend you a pickle ice cream.

As a Quora commenter put it whimsically, a Naive Bayes classifier that figured out that you liked pickles and ice cream would probably naively recommend you a pickle ice cream.

### Q8- Explain the difference between L1 and L2 regularization.

L2
regularization tends to spad 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 Laplacean prior on the terms,
while L2 corresponds to a Gaussian prior.

### Q9- What’s your favorite algorithm, and can you explain it to me in less than a minute?

This
type of question tests your understanding of how to communicate complex
and technical nuances with poise and the ability to summarize quickly
and efficiently. Make sure you have a choice and make sure you can
explain different algorithms so simply and effectively that a
five-year-old could grasp the basics!

### Q10- What’s the difference between Type I and Type II error?

Don’t
think that this is a trick question! Many machine learning interview
questions will be an attempt to lob basic questions at you just to make
sure you’re on top of your game and you’ve ppared all of your bases.

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 pgnant, while Type II error means you tell a pgnant woman she isn’t carrying a baby.

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 pgnant, while Type II error means you tell a pgnant woman she isn’t carrying a baby.

### Q11- What’s a Fourier transform?

A
Fourier transform is a generic method to decompose generic functions
into a superposition of symmetric functions. Or as this more intuitive
tutorial puts it, given a smoothie, it’s how we find the recipe. The
Fourier transform finds the set of cycle speeds, amplitudes and phases
to match any time signal. A Fourier transform converts a signal from
time to frequency domain — it’s a very common way to extract features
from audio signals or other time series such as sensor data.

### Q12- 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 repsents an
unsupervised learning algorithm that learns repsentations of data
through the use of neural nets.

### Q14- 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.

### Q15- 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.

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]

### Q16- 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: replace each node. If it doesn’t decrease predictive accuracy, keep it pruned. While simple, this heuristic actually comes pity close to an approach that would optimize for maximum accuracy.

Reduced error pruning is perhaps the simplest version: replace each node. If it doesn’t decrease predictive accuracy, keep it pruned. While simple, this heuristic actually comes pity close to an approach that would optimize for maximum accuracy.

### Q17- Which is more important to you– model accuracy, or model performance?

This
question tests your grasp of the nuances of machine learning model
performance! Machine learning interview questions often look towards the
details. There are models with higher accuracy that can perform worse
in pdictive power — how does that make sense?

Well, it has everything to do with how model accuracy is only a subset of model performance, and at that, a sometimes misleading one. For example, if you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely p dict no fraud at all if only a vast minority of cases were fraud. However, this would be useless for a predictive model — a model designed to find fraud that asserted there was no fraud at all! Questions like this help you demonstrate that you understand model accuracy isn’t the be-all and end-all of model performance.

Well, it has everything to do with how model accuracy is only a subset of model performance, and at that, a sometimes misleading one. For example, if you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely p dict no fraud at all if only a vast minority of cases were fraud. However, this would be useless for a predictive model — a model designed to find fraud that asserted there was no fraud at all! Questions like this help you demonstrate that you understand model accuracy isn’t the be-all and end-all of model performance.

### Q18- 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.

### Q19- 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:

- Collect more data to even the imbalances in the dataset.
- Re sample the dataset to correct for imbalances.
- Try a different algorithm altogether on your dataset.

### Q20- When should you use classification over regression?

Classification
produces discrete values and dataset to strict categories, while
regression gives you continuous results that allow you to better
distinguish differences between individual points. You would use
classification over regression if you wanted your results to reflect the
belongingness of data points in your dataset to certain explicit
categories (ex: If you wanted to know whether a name was male or female
rather than just how correlated they were with male and female names.)

### Q21- Name an example where ensemble techniques might be useful.

Ensemble
techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and
make the model more robust (unlikely to be influenced by small changes
in the training data).

You could list some examples of ensemble methods, from bagging to boosting to a “bucket of models” method and demonstrate how they could increase pdictive power.

You could list some examples of ensemble methods, from bagging to boosting to a “bucket of models” method and demonstrate how they could increase pdictive power.

### Q22- How do you ensure you’re not overfitting with a model?

This
is a simple restatement of a fundamental problem in machine learning:
the possibility of overfitting training data and carrying the noise of
that data through to the test set, thereby providing inaccurate
generalizations.

There are three main methods to avoid overfitting:

There are three main methods to avoid overfitting:

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

### Q23- 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: here is a
fairly comphensive list. You could use measures such as the F1 score,
the accuracy, and the confusion matrix. What’s important here is to
demonstrate that you understand the nuances of how a model is measured
and how to choose the right performance measures for the right
situations.

### Q24- How would you evaluate a logistic regression model?

A
subsection of the question above. You have to demonstrate an
understanding of what the typical goals of a logistic regression are
(classification, pdiction etc.) and bring up a few examples and use
cases.

### Q25- 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. 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.

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