10+ Mediterranean Shipping Company Interview Questions and Answers

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

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

SVM stands for Support Vector Machine, a supervised machine learning algorithm used for classification and regression tasks.

• SVM finds the hyperplane that best separates different classes in the feature space.

• It can handle both linear and non-linear data by using different kernel functions.

• Example project: Sentiment analysis using SVM to classify movie reviews as positive or negative.

Random forest is an ensemble learning method used for classification and regression tasks, consisting of multiple decision trees.

• Random forest is made up of multiple decision trees, where each tree is built using a subset of the training data and a random subset of features.

• During prediction, each tree in the forest independently predicts the output, and the final output is determined by a majority vote (classification) or averaging (regression) of all the trees' predictions....read more

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

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

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

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

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