AiRobosoft Interview Questions and Answers

Bias variance tradeoff is a key concept in machine learning that deals with the balance between underfitting and overfitting.

• Bias refers to the error that is introduced by approximating a real-life problem, while variance refers to the amount by which the estimate of the target function will change if different training data was used.

• High bias means the model is too simple and underfits the data, while high variance me...read more

Ensemble learning is a technique of combining multiple machine learning models to improve the overall performance.

• Ensemble learning can be done in two ways: bagging and boosting.

• Bagging involves training multiple models independently on different subsets of the data and then combining their predictions.

• Boosting involves training models sequentially, with each model trying to correct the errors of the previous model.

• Ens...read more

Transformers are models that process sequential data by learning contextual relationships between words.

• Transformers are a type of deep learning model commonly used in natural language processing tasks.

• They are based on the attention mechanism, allowing them to focus on different parts of the input sequence.

• Examples of transformer models include BERT, GPT, and TransformerXL.

L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting by adding penalty terms to the loss function.

• L1 regression adds the absolute values of the coefficients as penalty term (Lasso regression)

• L2 regression adds the squared values of the coefficients as penalty term (Ridge regression)

• L1 regularization can lead to sparse models with some coefficients being exactly zero

• L2 regul...read more

AUC (Area Under the Curve) is a metric that measures the performance of a classification model. ROC (Receiver Operating Characteristic) is a graphical representation of the AUC.

• AUC is a single scalar value that represents the area under the ROC curve.

• ROC curve is a plot of the true positive rate against the false positive rate for different threshold values.

• AUC ranges from 0 to 1, where a higher value indicates better ...read more

Parameter of random forest is the number of trees in the forest.

• Number of trees in the forest affects model performance

• Higher number of trees can lead to overfitting

• Commonly tuned parameter in random forest algorithms

p, d, q values are parameters used in ARIMA time series models to determine the order of differencing and moving average components.

• p represents the number of lag observations included in the model (autoregressive order)

• d represents the degree of differencing needed to make the time series stationary

• q represents the number of lagged forecast errors included in the model (moving average order)

• For example, in an ARIMA(1,