The overall parameters have been divided into 3 categories by XGBoost authors:

General Parameters: Guide the overall functioning
Booster Parameters: Guide the individual booster (tree/regression) at each step
Learning Task Parameters: Guide the optimization performed

The various steps to be performed for Parameter Tuning are:

1) Choose a relatively high learning rate....

1) Hyperparameters are certain values or weights that determine the learning process of an algorithm.

2) XGBoost provides large range of hyperparameters. We can leverage the maximum power of XGBoost by tuning its hyperparameters.

3) The most powerful ML algorithm like XGBoost is famous for picking up patterns and regularities in the data by automatically tuning thousands of learna...

Ridge and Lasso regression uses two different penalty functions. Ridge uses L2 where as lasso go with L1. In ridge regression, the penalty is the sum of the squares of the coefficients and for the Lasso, it’s the sum of the absolute values of the coefficients. It’s a shrinkage towards zero using an absolute value (L1 penalty) rather than a sum of squares(L2 penalty).

As we know that ridg...

Fitting a time series forecasting model requires 5 steps . The steps are explained below :

1) Data preparation : Data preparation is usually the first step where we load all the essential packages and data into a time series object.

2) Time series decomposition : Decomposition basically means deconstructing and visualizing the series into its component parts.

3) Modelling :...

CNN : Convolutional layers . CNNs have unique layers called convolutional layers which separate them from RNNs and other neural networks. Within a convolutional layer, the input is transformed before being passed to the next layer. A CNN transforms the data by using filters.

RNN : Recurrent neural networks are networks that are designed to interpret temporal or sequential information. RN...

Outlier values, or simply outliers, are data points in statistics that don’t belong to a certain population. An outlier value is an abnormal observation that is very much different from other values belonging to the set.

Identification of outlier values can be done by using univariate or some other graphical analysis method. Few outlier values can be assessed individually but assessing a...

Tip 1 : The cross questioning can go intense some time, think before you speak.
Tip 2 : Be open minded and answer whatever you are thinking, in these rounds I feel it is important to have opinion.
Tip 3 : Context of questions can be switched, pay attention to the details. It is okay to ask questions in these round, like what are the projects currently the company is investing, which team ...