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.

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.

Explain how a ROC curve works.

The ROC 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).

Define precision and recall.

The recall is also known as the true positive rate: the number 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 number 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.

What’s the difference between Type I and Type II errors?

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.

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.

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 pretty close to an approach that would optimize for maximum accuracy.

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.

How would you handle an imbalanced dataset?

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.

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 (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.

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

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.

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.

What is the difference between deep learning and machine learning?

Machine Learning involves algorithms that learn from patterns of data and then apply it to decision making. Deep Learning, on the other hand, is able to learn through processing data on its own and is quite similar to the human brain where it identifies something, analyzes it, and makes a decision.
The key differences are as follow:

• The manner in which data is presented to the system.
• Machine learning algorithms always require structured data and deep learning networks rely on layers of artificial neural networks.

How are covariance and correlation different from one another?

Covariance measures how two variables are related to each other and how one would vary with respect to changes in the other variable. If the value is positive it means there is a direct relationship between the variables and one would increase or decrease with an increase or decrease in the base variable respectively, given that all other conditions remain constant.

Correlation quantifies the relationship between two random variables and has only three specific values, i.e., 1, 0, and -1.

1 denotes a positive relationship, -1 denotes a negative relationship, and 0 denotes that the two variables are independent of each other.

Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?

One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.

Explain Logistic Regression and its assumptions.

Logistic Regression is a go-to method for classification. Logistic regression models the probability of the default class (e.g. the first class). It employs the use of the sigmoid function that can take any real-valued number and map it into a probability value between 0 and 1 to predict the output class. There are two types of logistic regression: Binary and Multinomial. Binary Logistic Regression deals with two categories whereas multinomial deals with three or more categories.

Assumptions:

Binary logistic regression requires the dependent variable to be binary. The independent variables should be independent of each other. That is, the model should have little or no multicollinearity, The independent variables should be linearly related to the log odds.

How do you split your data between training and validation?

Training and validation set from data can be split on the following 2 principles. First, ensure the validation set is large enough to yield statistically meaningful results. Second, the validation set should be representative of the data set as a whole. In other words, don't pick a validation set with different characteristics than the training set.  An optimal way would be to use k-folds validation. This method makes multiple splits of the dataset into train and validation sets. This offers various samples of data and ultimately reduces the chances of overfitting.

Describe Binary Classification.

Binary classification is the process of predicting the class of a given set of data points. These classes are also known as targets/ labels. This method of predictive modeling approximates a mapping function (f) from input variables (X) to discrete output variables (y). For example, spam detection in email service providers can be identified as a classification problem. This is binary classification since there are only 2 classes: spam and not spam.

Is it necessary to use activation functions in neural networks?

Activation functions are essential to learn and model complex data and its relationships. These functions add non-linearity to the network. If there is no activation function then the input signal will be mapped to output using a linear function which is just a polynomial of one degree. Now, why is that a problem? Linear functions are not able to capture complex functional mappings of the data. However, this is possible with the use of nonlinear functions which have a degree of more than one. These activation functions can be used to model any real-world data.