DP-100 Exam Dumps - Designing and Implementing a Data Science Solution on Azure

The Export Count Table module is provided for backward compatibility with experiments that use the Build Count Table (deprecated) and Count Featurizer (deprecated) modules.

E: Summarize Data statistics are useful when you want to understand the characteristics of the complete dataset. For example, you might need to know:

How many missing values are there in each column?

How many unique values are there in a feature column?

What is the mean and standard deviation for each column?

The module calculates the important scores for each column, and returns a row of summary statistics for each variable (data column) provided as input.

Box 1: training_data

The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.

Box 2: validation_data

Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.

Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.

dataset = Dataset.Tabular.from_delimited_files(data)

training_data, validation_data = dataset.random_split(percentage=0.8, seed=1)

automl_config = AutoMLConfig(compute_target = aml_remote_compute,

task = 'classification',

primary_metric = 'AUC_weighted',

training_data = training_data,

validation_data = validation_data,

label_column_name = 'Class'

)

Box 3: label_column_name

label_column_name:

The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.

This parameter is applicable to training_data and validation_data parameters.

Step 1: Train Model

Two-Class Boosted Decision Tree

First, set up the boosted decision tree model.

1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.

2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.

The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.

3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).

This portion of the experiment now looks something like this:

Step 2: Score Model

Score and evaluate the models

You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.

Add the Score Model modules

1. Find the Score Model module and drag it onto the canvas.

2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.

3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

Step 3: Evaluate Model

To evaluate the two scoring results and compare them, you use an Evaluate Model module.

1. Find the Evaluate Model module and drag it onto the canvas.

2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.

3. Connect the other Score Model module to the right input port.

Box 1: No

max_total_runs (50 here)

The maximum total number of runs to create. This is the upper bound; there may be fewer runs when the sample space is smaller than this value.

Box 2: Yes

Policy EarlyTerminationPolicy

The early termination policy to use. If None - the default, no early termination policy will be used.

Box 3: No

Discrete hyperparameters are specified as a choice among discrete values. choice can be:

one or more comma-separated values

a range object

any arbitrary list object