When it comes to a job interview, it is essential to know the questions that one can face and frame the correct answers.
I am building a list of questions with varying difficulty levels to help you prepare for data science and machine learning job interviews.
Github pages version of this repository is available on here
Questions related to traditional machine learning and basics of data science.
Answer
<style> .tablelines table, .tablelines td, .tablelines th { border: 1px solid black; } </style>| Supervised ML | Unsupervised ML |
|---|---|
| Requires labelled training data | Requires unlabelled training data |
| The algorithm tries to predict based on what it learned from training data. | It is up to the algorithm tries to find structure/pattern in the data. |
| Types: Classification, Regression | Clustering, Association rule learning |
| Decision Tree, Naive Bayes, Random Forest | K-Means clustring, Apriori algorithm |
| {: .tablelines} |
Answer A confusion matrix is a table often used to understand the performance of a machine learning classification algorithm on a test set where we have the correct values. It not only tells us how many records the algorithm predicted correctly but breaks the performance into four sections:
- True positive- Algorithm predicted say 'Yes' and actual value is 'Yes'
- True negative- Algorithm predicted 'No' and actual values is 'No'
- False positive- 'Yes' was predicted, but actual value was 'No'
- False positive- 'No' was predicted but actual value was 'Yes'
Use cases:
- A false positive in a drug test can result in jail for an innocent person.
- A false negative in a covid-19 test can lead to an infected person remaining untreated and spreading the disease.
Answer Decision trees belong to a family of supervised machine learning algorithms. It can be used on both classification and regression problems.
A decision tree uses the training data to learn decision rules to predict the target variable. It builds the model in the form of a tree structure with a root node at its top, followed by child nodes and leaf nodes at the bottom. The best predictor of the target variable becomes the root node and is determined using Gini impurity. Subsequent child nodes are decided similarly.
Finally, the leaves at the bottom represent the output of the model.
A decision tree classifier breaks the dataset into smaller and smaller subsets and creates the tree from the top (also called root node) by asking simple decision-based questions.
Answer Overfitting- A model is said to overfit when it performs well on the training set but poorly on the test/dev set. An overfit model has learned from the noise of the data and makes predictions based on that noise. One way to prevent overfitting is to penalize parameters that lead to overfitting.
Underfitting- When a model performs poorly on both training and test data, it is called underfitting. An underfit model is unable to grasp the underlying trends that are needed to make correct predictions. It performs poorly even on previously seen data. One way to avoid underfitting is to increase the complexity of the model.
Answer Bias: The bias error is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs. High bias leads to underfitting and poor performance on both training and testing data. It is the difference between the predicted value and the actual value in the test set.
Variance: The variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs. This phenomenon is also known as overfitting. An overfit model has poor performance on the testing set but high performance on the training set.
Answer If our model is too simple and has fewer parameters, it can have high bias and low variance. Then again, if a model is complex and has too many parameters, it's likely to have high variance and low bias. If one tries to decrease the bias, it increases its variance and vice-versa. This relationship between bias and variance is called the bias-variance tradeoff.
Answer If our model performs better on the training data and poorly on the test/unseen data, it is a sign of overfitting. Some popular techniques for avoiding overfitting are:
- Cross-validation
- Increase training data
- Reducing model complexity by reducing parameters (say decreasing the number of splits, etc in a decision tree)
- Early stopping
- Regularization
Answer Often our dataset has some rows where the values are either missing (null/NaN/None or blank) or unreliable data. We should handle these before trying to train a model:
- Replace with a mean/median of the feature in question. Sometimes we can also use the mean of a specific group. For example, in a column of salary, we can replace it with the average salary within that post/skillset instead of the whole column.
- Replace with the most common (mode) value.
- Replace with zero or another constant based on expert knowledge of the field. For example, in a column of the number of reviews, we can replace it with zero if it is missing. The intuition is that if a product has missing data in the 'number of reviews' column, it is likely to have never been reviewed.
- We can also use sklearn's KNNImputer to impute missing values.
Answer
| KNN | K-Means |
|---|---|
| It is a supervised classification/regression algorithm. | It is an unsupervised clustering algorithm. |
| Predicts the class, etc by combining the class of k nearest data points. | Tries to find groups of similar datapoints and creates k such clusters. |
Answer Data normalization is a technique often used during the data preparation phase of building a machine learning model. We use it to shift the values of some features in a dataset into the same scale so that our model understands the importance of these features and does not lean towards certain features just because they have larger values.
It is usually required when features have different ranges or units (of measurement).
For example, we have a dataset where Age and Salary are two features. Now the age ranges from 18 to 55, whereas the Salary ranges from 20,000 to 5,00,000. We can see the difference in values and ranges in the two features. Our model is likely to be more affected by the Salary feature because it has larger values. But this does not necessarily mean that one is more important than the other. Once we normalize these features and bring them to the same scale, the model's prediction accuracy is likely to improve.
Answer We need features to predict our target variable. The phrase Curse of Dimensionality refers to the increase in error when our dataset has too many features. Theoretically, more dimensions allow for more information, but this rarely helps, as it adds unwanted noise. Machine learning algorithms are harder to train on data with high dimensions and often result in low accuracy and high running time. To avoid this, we should use dimensionality reduction techniques like feature selection using PCA (principal component analysis).
Q 12 . Define feature selection. What algorithm or techniques are generally used to perform feature selection.
Answer While building a machine learning model, it’s quite rare that all features are needed. The process by which we reduce the number of input features is called feature selection. It not only reduces noise in the data but also reduces the computational cost of developing the model.
Some popular feature selection methods:
- Principal component analysis
- Filtering features with high correlation (like Pearson correlation) to the target variable
- Feature importance with respect to the target variable
- Wrapper methods like Forward feature selection and Backward elimination
Answer While working with categorical data, we must convert them into a form that our machine learning algorithm can understand. One hot encoding is one such method used to encode categorical data. Here each unique category is represented by its binary column. We place a ‘1’ in the binary column for that category and ‘0’ values for the others.
For example, we have a categorical column in our data with three unique values: 'red', 'yellow', and 'green'. To one hot encode this categorical column, we split it into three separate columns representing each category. Rows that belong to the ‘red’ category will have ‘1’ in their binary column and a ‘0’ value for the others.
Answer
Answer Precision is defined as the proportion of positive identifications that were actually correct. Recall is defined as the proportion of actual positives that were identified correctly.
Answer
Answer
Answer
Answer
Q 20 . We know that one hot encoding increases the dimensionality of a dataset, but label encoding doesn’t. How?
Answer
Answer
Answer
Answer
Answer
Answer