Bela Instruments Interview Questions and Answers

XgBoost algorithm uses a greedy approach to determine splits based on feature importance.

• XgBoost algorithm calculates the information gain for each feature to determine the best split.

• The feature with the highest information gain is chosen for the split.

• This process is repeated recursively for each node in the tree.

• Features can be split based on numerical values or categories.

• Example: If a feature like 'age' has the highest information gain, the data will be split based on di...read more

Precision and recall are metrics used in evaluating the performance of classification models.

• Precision measures the accuracy of positive predictions, while recall measures the ability of the model to find all positive instances.

• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• For example, in a spam email detection system, high precision is desired to avoid classif...read more

Activation function is a mathematical function used in neural networks to introduce non-linearity.

• Activation function is applied to the weighted sum of inputs in a neural network node.

• It helps in determining the output of a node or the activation of a neuron.

• Common activation functions include sigmoid, tanh, ReLU, and softmax.

• Activation functions introduce non-linearity, allowing neural networks to learn complex patterns.

• They help in improving the accuracy and performance of ...read more

SMOTE stands for Synthetic Minority Over-sampling Technique, used to balance imbalanced datasets by generating synthetic samples.

• SMOTE is commonly used in machine learning to address class imbalance by creating synthetic samples of the minority class.

• It works by generating new instances of the minority class by interpolating between existing instances.

• SMOTE is particularly useful in scenarios where the minority class is underrepresented and traditional sampling techniques may...read more

Yes, I have experience working on cloud platforms such as AWS and Google Cloud.

• Experience with AWS services like S3, EC2, and Redshift

• Familiarity with Google Cloud services like BigQuery and Compute Engine

• Utilized cloud platforms for data storage, processing, and analysis

Entropy is a measure of randomness or uncertainty in a dataset, while information gain is the reduction in entropy after splitting a dataset based on a feature.

• Entropy is used in decision tree algorithms to determine the best feature to split on.

• Information gain measures the effectiveness of a feature in classifying the data.

• Higher information gain indicates that a feature is more useful for splitting the data.

• Entropy is calculated using the formula: -p1*log2(p1) - p2*log2(p2...read more

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.

• The null hypothesis is assumed to be true until there is enough evidence to reject it.

• Statistical tests are used to determine the likelihood of observing the data if the null hypothesis is true.

• The p-value is used to determine the significance of the results.

• Common hypothesis tests in...read more

Data imbalance refers to unequal distribution of classes in a dataset, where one class has significantly more samples than others.

• Data imbalance can lead to biased models that favor the majority class.

• It can result in poor performance for minority classes, as the model may struggle to accurately predict them.

• Techniques like oversampling, undersampling, and using different evaluation metrics can help address data imbalance.

• For example, in a fraud detection dataset, the majorit...read more