How to hide all the components Except the selected components which is to be shown?
• This is to be written on the Dashboard script area.
• This script will hide all the components except those which are mentioned here(“( [ "Bar_1", "Column_2" ] )”)

sdk.hideAllComponentExcept( [ "Bar_1", "Column_2" ] );

Steps to use DS LAB component to Export the  python script:-

1) In Admin module we need to Create a project first
2)In Ds lab module the project has been created 
3)Create Notebook 
4)add Data set as in put
5)Enter the script in Notebook and run
6)Save it after execution
7)Export it to pipeline,while exporting there will be a select option to select the required code,
8)After exporting in pipeline,in Ds lab component we need to configure to get the exported script from DS lab notebook,

  meta info tab:

 a)Execution type: Script runner
 b)Function input type: Data frame
 c)project name: abc
 d)script name(which has been created in DS lab notebook): xyz
 e)start function name: function()
  
*when we configure this all the script will be displayed itself which we have created in DS lab notebook

Can we use html tags to style components?

Yes. We can use  html tags to style the component.

Script for padding in title or description thru html tags.

Component Title : <span style = "color:#5adf14;font-size:15px;"> 
Potential Excess Opportunity </span>
 
Write this in respective connection script

var a = sdk.getWidget( 'timeline4' );
a.m_enablehtmlformate.subtitle = true;
a.m_enablehtmlformate.title = true;
a.draw();

When a model performs well on training data but poorly on untried data, this is over-fitting. 

Over-fitting is a very common problem in Machine Learning. There is an extensive range of literature dedicated to studying methods for preventing over-fitting.
Three simple steps to avoid Over-fitting:

• Train with more data - Training with more data can help algorithms detect the signal better (But, it won’t work every time).
  
• Keep the model simple - take fewer variables into account, thereby removing some of the noise in the training data
  
• Utilize cross-validation methods, such as the k-folds method.
  
• Use regularization techniques - Regularization describes a wide range of methods for forcibly simplifying your model, such as LASSO, that penalize certain model parameters if they're likely to cause over-fitting.
  
• Ensembling - Machine learning techniques called ensembles are used to combine predictions from various independent models.
  

How to show the components Except selected components which are to be not shown?

·        This is to be written on the Dashboard script area.
·        This script will show the components except those which are mentioned here(“( [ "Bar_1", "Column_2" ] )”).

sdk.showAllComponentExcept( [ "Bar_1", "Column_2" ] );

How to create a new user account?

Suppose we are creating data set in that we want to show the data based on selected row in a column:-


  1)select name,gender,sum(amount) as total from restaurant_data where gender =@gender@

    so when we run this query in data set it gives gender column data=Male and female ,based on select it shows data accordingly..

    All filter


     2) select name,app,gender,sum(amount) as total from restaurant_data where (app=@app@ or "All"=@app@)

    * it means if we select ALL in filter ,it shows data for all Apps,,

How to share an open doc link with a user?

How to activate an expired user account?

Selecting an individual group from a population to study the whole population.

 
Especially when dealing with larger datasets, data analysis cannot be performed on the entire volume of data at once. It becomes essential to collect certain data samples that may be analysed and used to represent the entire population. While doing this, it is imperative to carefully select sample data from the enormous data collection that accurately reflects the complete dataset.
 
Sampling - 2 broad categories: 

• Probability sampling - Random selection techniques are used to select the sample.
  
• Non-probability sampling - Non-random selection techniques based on certain criteria are used to select the sample.