Date: 09-05-2025
Company name: Tredence
Role: Data Scientist

01. What is the exploding gradient problem while using the back propagation technique?

Answer: When large error gradients accumulate and result in large changes in the neural network weights during training, it is called the exploding gradient problem. The values of weights can become so large as to overflow and result in NaN values. This makes the model unstable and the learning of the model to stall just like the vanishing gradient problem. This is one of the most commonly asked interview questions on machine learning.

02. What do you mean by Associative Rule Mining (ARM)?

Answer- Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.

03. What is the primary difference between R square and adjusted R square?

Answer- In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.

04. What are hard margin and soft Margin SVMs?

Answer- Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.

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Date: 13-06-2025
Company name: Infosys
Role: Data Scientist

01. Can you explain how the memory cell in an LSTM is implemented computationally?

Answer- The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.

02. Explain some cases where k-Means clustering fails to give good results:

Answer-
k-means has trouble clustering data where clusters are of various sizes and densities.

Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.

If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.

03. What is the difference between a Multiclass problem and a Multilabel problem?

Answer- Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.
Multilabel classification assigns to each sample a set of target labels. This can be thought of as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.

04. Explain Categorical data in Pandas?

Answer- Categorical data is a discrete set of values for a particular outcome and has a fixed range. Also,
the data in the category need not be numerical, it can be textual in nature. Examples are
gender, social class, blood type, country affiliation, observation time, etc. There is no hard and
fast rule for how many values a categorical value should have. One should apply one’s domain
knowledge to make that determination on the data sets.


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