Date: 14/12/2023
Company name: Datanyze
Role: ML Engineer
Topic: ROC, K-Means, P-Value, Supervised and semi-Supervised ML

1. Explain how a ROC curve works.
Answer: 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).

2. How can you select K for K-means Clustering?
There are two kinds of methods that include direct methods and statistical testing methods:
• Direct methods: It contains elbow and silhouette
• Statistical testing methods: It has gap statistics.
The silhouette is the most frequently used while determining the optimal value of k

3. What is P-value?
P-values are used to make a decision about a hypothesis test. P-value is the minimum significant level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis.

4. What is Semi-supervised Machine Learning?
Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.
In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.

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Date: 19-12-2023
Company name: IKEA
Role: Data Analyst
Topic: Statistics, SQL

1. How can we deal with problems that arise when the data flows in from a variety of sources?

There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of:

Identifying the presence of similar/same records and merging them into a single recordRe-structuring the schema to ensure there is good schema integration



2. Where is Time Series Analysis used?

Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role:

Statistics
Signal processing
Econometrics
Weather forecasting
Earthquake prediction
Astronomy
Applied science


3. What are the ideal situations in which t-test or z-test can be used?

It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases.


4. What is the usage of the NVL() function?

The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.


5. What is the difference between DROP and TRUNCATE commands?

If a table is dropped, all things associated with that table are dropped as well. This includes the relationships defined on the table with other tables, access privileges, and grants that the table has, as well as the integrity checks and constraints.

However, if a table is truncated, there are no such problems as mentioned above. The table retains its original structure and the data is dropped.

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Date - 20-12-2023
Company name: CRED
Role: Data Scientist
Topic: sets n groups, tpr-fpr, triggers, bag of words

1. What do Tableau's sets and groups mean?

Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.


3.What do you mean by a Bag of Words (BOW)?
It is used for word frequency or occurrences to train a classifier.
It contains a text representation that describes the frequency with which words appear in a document.
It has two steps:
-A list of terms that are well-known.
-A metric for determining the existence of well-known terms.

3. What are Nested Triggers?

Triggers may implement DML by using INSERT, UPDATE, and DELETE statements. These triggers that contain DML and find other triggers for data modification are called Nested Triggers.

4. What is a True positive rate and a false positive rate?

True positive rate or Recall: It gives us the percentage of the true positives captured by the model out of all the Actual Positive class.

TPR = TP/ (TP+FN)

False Positive rate: It gives us the percentage of all the false positives by my model prediction from the all Actual Negative class.

FPR = FP/(FP+TN)

Date: 21-12-2023
Company name: Biocon
Role: ML Engineer
Topic: nodes, adaboost, ensemble, hierarchical

1. List down the different types of nodes in Decision Trees.

The Decision Tree consists of the following different types of nodes:
1. Root node: It is the top-most node of the Tree from where the Tree starts.
2. Decision nodes: One or more Decision nodes that result in the splitting of data into multiple data segments and our main goal is to have the children nodes with maximum homogeneity or purity.
3. Leaf nodes: These nodes represent the data section having the highest homogeneity.

2. What is the Alpha Term in AdaBoost? How does it work?

In AdaBoost, multiple weak learners are trained to get the strong learner. As a result, hence calculating the error term of every weak learner is essential to know which weak learner is performing best and which is not.
The term Alpha is a parameter that indicates the weight that should be given to a particular weak learner algorithm. If the value of the term Alpha for a particular algorithm is high, that indicates that the model is performing best and the error rate for the same is low.

3. Since Ensemble Learning provides better output most of the time, why do you not use it all the time?

Although it provides a better outcome many times, it is not true that it will always perform better. There are several ensemble methods, each with its own advantages/disadvantages, and choosing one to use depends on the problem at hand. If there are models with high variance, then it will benefit from bagging. If the model is biased, it is better to use boosting. If the work is in probabilistic setting, the ensemble methods may not work because it is known that boosting delivers poor probability estimates.


4. What are the various types of Hierarchical Clustering?

The two different types of Hierarchical Clustering technique are as follows:
Agglomerative: It is a bottom-up approach, in which the algorithm starts with taking all data points as single clusters and merging them until one cluster is left.
Divisive: It is just the opposite of the agglomerative algorithm as it is a top-down approach.

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#QUIZ

You are working on a deep learning project to classify images of animals. You've trained a convolutional neural network (CNN) on a large dataset of labeled images. However, after deployment, you notice that the model's performance on real-world data is significantly lower than on the training and validation datasets.

What could be the most likely reason for this discrepancy?

A. Overfitting to the training data.
B. Underfitting due to insufficient training epochs.
C. The learning rate for the training was set too high.
D. The real-world data contains classes not present in the training set.

Date - 23-12-23
Company: Splunk
Role: Data Scientist


1. How Are Weights Initialized in a Neural network?

Ans: There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.

Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.


2. What are the variants of Gradient descent?

Ans: Stochastic Gradient Descent: We use only a single training example for calculation of gradient and
update parameters.
Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at
each iteration.
Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of
Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is
used.


3. What are the feature selection methods used to select the right variables?

Ans: There are two main methods for feature selection:
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is "bad data in, bad answer out." When we're limiting or selecting the features, it's all about selecting the useful feature.

Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together. Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.

4. What is joint sampling and separate sampling?

Ans:
· Joint sampling is done when there are equal number of events and non-events. Not appropriate for imbalanced data
· Separate sampling is done for imbalanced data. For rare event, all observations are kept when target = 1 and only few observations are kept when target = 0.

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Date: 26-12-2023
Company name: Wipro
Role: Data Analyst
Topic: concurrency, checkpoint, groups, nested if, recursive stored procedure

1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking

2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.

3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.

4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.

5. What is a Recursive Stored Procedure?

A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.

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Date: 27/12/2023
Company name: Walmart
Role: ML Engineer
Topic: machine learning

1. Explain the difference between L1 and L2 regularization.
Answer: 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 Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.

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

Answer: 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]

3. What’s the “kernel trick” and how is it useful?

Answer: 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.

4. Explain how a ROC curve works.

Answer: 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).

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Q. How can outlier values be treated?

A. An outlier is an observation in a dataset that differs significantly from the rest of the data. This signifies that an outlier is much larger or smaller than the rest of the data.
Given are some of the methods of treating the outliers: Trimming or removing the outlier, Quantile based flooring and capping, Mean/Median imputation.


Q. What is root cause analysis?

A. A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.


Q. What is bias and variance in Data Science?

A. The model's simplifying assumptions simplify the target function, making it easier to estimate. Bias is the difference between the Predicted Value and the Expected Value in its most basic form. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data. In contrast to bias, variance occurs when the model takes into account the data's fluctuations, or noise.


Q. What is a confusion matrix?

A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. This gives us the following: "True positive" for event values that were successfully predicted. "False positive" for event values that were mistakenly predicted. For successfully anticipated no-event values, "true negative" is used. "False negative" for no-event values that were mistakenly predicted.

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