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Bluebird Solar Interview Questions and Answers

Updated 13 Jun 2024
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Q1. What is linear regression and logistics regression?

Ans.

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.

  • Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.

  • Linear regression assumes a linear relationship between the independent and dependent variables, while logistic regression uses a logistic funct...read more

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Q2. What is central limit theorem? Why we use it

Ans.

Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

  • Central Limit Theorem is used to make inferences about a population mean based on the sample mean.

  • It allows us to use the properties of the normal distribution to estimate population parameters.

  • It is essential in hypothesis testing and constructing confidence intervals.

  • For example, if we take multiple samples of a population and calculat...read more

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Q3. What is support vector machine?

Ans.

Support Vector Machine is a supervised machine learning algorithm used for classification and regression tasks.

  • Support Vector Machine finds the hyperplane that best separates different classes in the feature space

  • It works by maximizing the margin between the hyperplane and the nearest data points, known as support vectors

  • SVM can handle both linear and non-linear data by using different kernel functions like linear, polynomial, and radial basis function kernels

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Q4. Difference between Random and ordering partition

Ans.

Random partition involves splitting data randomly, while ordering partition involves splitting data based on a specific order.

  • Random partition randomly divides data into subsets without any specific order.

  • Ordering partition divides data into subsets based on a specific order, such as time or alphabetical order.

  • Random partition is useful for creating training and testing sets for machine learning models.

  • Ordering partition is helpful for time series data analysis or when data n...read more

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Q5. Difference between Logistic and Linear Regression

Ans.

Logistic regression is used for binary classification while linear regression is used for regression tasks.

  • Logistic regression predicts the probability of a binary outcome (0 or 1) based on one or more independent variables.

  • Linear regression predicts a continuous outcome based on one or more independent variables.

  • Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regression uses a linear function.

  • Logistic regression is commonly u...read more

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Q6. What is linear and logistics.?

Ans.

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.

  • Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.

  • In linear regression, the relationship between the independent and dependent variables is assumed to be linear, while in logistic regression, th...read more

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Q7. Difference between KNN and K Means

Ans.

KNN is a supervised learning algorithm used for classification and regression, while K Means is an unsupervised clustering algorithm.

  • KNN stands for K-Nearest Neighbors and assigns a class label based on majority voting of its k-nearest neighbors.

  • K Means is a clustering algorithm that partitions data into k clusters based on similarity.

  • KNN requires labeled data for training, while K Means does not need labeled data.

  • KNN is a lazy learner as it does not learn a discriminative fu...read more

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Q8. Range of Cross Entropy Loss

Ans.

Cross entropy loss measures the difference between two probability distributions.

  • Range of cross entropy loss is [0, infinity)

  • Lower values indicate better model performance

  • Commonly used in classification tasks

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