Info Edge Data Scientist Interview Questions, Process, and Tips

Q1. Explain all of decision tree and random forest?
Ans. 
Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.
• Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.

• Random forest is a ...read more
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Q2. Some basic probability questions
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Test 45 mins 30 ques

Q1. What is Linearregression
Ans. 
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that minimi...read more
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Q2. What is random forest
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Random forest is an ensemble learning method used for classification and regression tasks.
• Random forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.

• Random forest is robust to overfitting and noisy data, and it can handle large datasets...read more
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Q3. WHat is xgboost
Ans. 
XGBoost is an optimized distributed gradient boosting library designed for efficient and accurate large-scale machine learning.
• XGBoost stands for eXtreme Gradient Boosting.

• It is a popular machine learning algorithm known for its speed and performance.

• XGBoost is used for regression, classification, ranking, and user-defined prediction problems.

• It is based on the gradient boosting framework and uses decision trees as bas...read more
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Q1. Describe LSTM and GRU
Ans. 
LSTM and GRU are types of recurrent neural networks used for processing sequential data.
• LSTM (Long Short-Term Memory) networks are capable of learning long-term dependencies in data.

• GRU (Gated Recurrent Unit) networks are simpler than LSTM and have fewer parameters.

• LSTM has three gates (input, output, forget) while GRU has two gates (update, reset).

• LSTM is better at capturing long-term dependencies but is more complex,...read more
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Q2. Define Hypothesis Testing
Ans. 
Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• It aims to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

• Common methods of hypothesis testing include t-tests, chi-square tests, and ANOVA.

• The p-value is used to dete...read more
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Mcq based test on data science concepts

Q1. Explain precision,recall etc
Ans. 
Precision and recall are metrics used to evaluate the performance of classification models.
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• F1 score is the weighted average of precision and recall, where the best value is 1 and the worst is 0.

• Precision ...read more
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Q2. What is dropout in neural networks
Ans. 
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly setting some neuron outputs to zero during training.
• Dropout is a regularization technique used in neural networks to prevent overfitting.

• During training, a fraction of neurons are randomly selected and their outputs are set to zero.

• This helps prevent complex co-adaptations in neurons and improves generalization.

• Dropout is t...read more
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