The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.

Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.

Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.

Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words: ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.

Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

AUC.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

The box-plot algorithm can be used to display outliers.

One other way to quickly identify Outliers visually is to create scatter plots.

References:

https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/

Box 1: No

If a training cluster already exists it will be used.

Box 2: Yes

The wait_for_completion method waits for the current provisioning operation to finish on the cluster.

Box 3: Yes

Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.

Box 4: No

Need to use training_compute.delete() to deprovision and delete the AmlCompute target.

After you define your steps, you build the pipeline by using some or all of those steps.

# Build the pipeline. Example:

pipeline1 = Pipeline(workspace=ws, steps=[compare_models])

# Submit the pipeline to be run

pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1)

A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.

The run Class get_all_logs method downloads all logs for the run to a directory.

References:

https://machinel earningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/

Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.

Example:

from azureml.train.sklearn import SKLearn

script_params = {

# mount the dataset on the remote compute and pass the mounted path as an argument to the training script

'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),

'--regularization': 0.5

}

est = SKLearn(source_directory=script_folder,

script_params=script_params,

compute_target=compute_target,

environment_definition=env,

entry_script='train_mnist.py')

# Run the experiment

run = experiment.submit(est)

run.wait_for_completion(show_output=True)

See below image

Explanation

Use a solution with logging.info(message) instead.

Note: Python printing/logging example:

logging.info(message)

Destination: Driver logs, Azure Machine Learning designer

Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.

References:

https://docs.mi crosoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

framework_version: The PyTorch version to be used for executing training code.

Instead use Permutation Feature Importance Explainer (PFI).

Note 1: Mimic explainer is based on the idea of training global surrogate models to mimic blackbox models. A global surrogate model is an intrinsically interpretable model that is trained to approximate the predictions of any black box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the black box model.

Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.

Box 1: from azureml.core.webservice import Webservice

The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:

from azureml.core import Environment

from azureml.core.webservice import Webservice

from azureml.core.model import Model, InferenceConfig

Box 2: predictions = service.run(input_json)

Example: The following code demonstrates sending data to the service:

import json

test_sample = json.dumps({'data': [

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]

]})

test_sample = bytes(test_sample, encoding='utf8')

prediction = service.run(input_data=test_sample)

print(prediction)

The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.

Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.

Scenario:

Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.

Experiments for local crowd sentiment models must combine local penalty detection data.

All shared features for local models are continuous variables.

D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.

E: For Hidden layer specification, select the type of network architecture to create.

Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be

accomplished easily with only one or a few hidden layers.

References:

https://do cs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network

Text Description automatically generated with medium confidence

Box 1: training-dataset

Baseline dataset - usually the training dataset for a model.

Box 2: predictions-dataset

Target dataset - usually model input data - is compared over time to your baseline dataset. This comparison means that your target dataset must have a timestamp column specified.

The monitor will compare the baseline and target datasets.

Box 1: log

The number of observations in the dataset.

run.log(name, value, description='')

Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.

Example: run.log("accuracy", 0.95)

Box 2: log_image

A box plot of income by home_owner.

log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.

Example: run.log_image("ROC", plot=plt)

Box 3: log_table

A dictionary containing the city names and the average income for each city.

log_table: Log a dictionary object to the run with the given name.

Step 1: Implement a K-Means Clustering model

Step 2: Use the cluster as a feature in a Decision jungle model.

Decision jungles are non-parametric models, which can represent non-linear decision boundaries.

Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.

Scenario:

Ad response rated declined.

Ad response models must be trained at the beginning of each event and applied during the sporting event.

Market segmentation models must optimize for similar ad response history.

Ad response models must support non-linear boundaries of features.

References:

https://docs.microsoft.com/en-us/azure/machine-learning /studio-module-reference/multiclass-decision-jungle

https://doc s.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender

Box 1: from_run_id

from_run_id(workspace, experiment_name, run_id)

Create the client with factory method given a run ID.

Returns an instance of the ExplanationClient.

Parameters

• Workspace Workspace An object that represents a workspace.
• experiment_name str The name of an experiment.
• run_id str A GUID that represents a run.

Box 2: list_model_explanations

list_model_explanations returns a dictionary of metadata for all model explanations available.

Returns

A dictionary of explanation metadata such as id, data type, explanation method, model type, and upload time, sorted by upload time

Box 3: explanation

Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

References:

https://doc s.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

See below image

Explanation:

In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), is a statistic used to measure the ordinal association between two measured quantities.

It is a supported method of the Azure Machine Learning Feature selection.

Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules

You need to implement an early stopping criterion on models that provides savings without terminating promising jobs.

Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.

Example:

from azureml.train.hyperdrive import TruncationSelectionPolicy

early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5)

Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.

Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.

Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

References:

https://docs.microsoft.com/en-us/azure/machine-lear ning/studio-module-reference/feature-selection-modules

Step 1: Sweep Clustering

Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.

One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.

Step 2: Train Model

Step 3: Evaluate Model

Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.

You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

References:

http://breaking- bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

Scenario: Testing

You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Box 1: Assign to folds

Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.

Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

Box 2: Partition evenly

Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:

• Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.

Box 1: Mutual Information.

The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.

Box 2: MedianValue

MedianValue is the feature column, , it is the predictor of the dataset.

Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection

Box 1: Replace using MICE

Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.

Box 2: Propagate

Cols with all missing values indicate if columns of all missing values should be preserved in the output.

References:

https://doc s.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

Box 1: 500

For Random seed, type a value to use as seed for randomization. If you specify 0 (the default), a number is generated based on the system clock.

A seed value is optional, but you should provide a value if you want reproducibility across runs of the same experiment.

Here we must replicate the findings.

Box 2: Mean Absolute Error

Scenario: Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model’s accuracy and replicate the findings.

Regression. Choose one of the following: Precision, Recall, Mean Absolute Error , Root Mean Squared Error, Relative Absolute Error, Relative Squared Error, Coefficient of Determination

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance

Create Scatterplot

Summarize Data

Clip Values

You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.

References:

https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/

https://docs.microsoft.com/en-us/azure/machine-learning/studio-modu le-reference/clip-values

Step 1: Augment the data

Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.

Step 2: Add the Bayesian Linear Regression module.

Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.

Step 3: Configure the regularization weight.

Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.

Scenario:

Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Explanation:

Box 1: Accuracy

Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.

Box 2: R-Squared

Box 1: Floating point

Need floating point for Median values.

Scenario: An initial investigation shows that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format.

Box 2: Unchanged

Note: Select the Categorical option to specify that the values in the selected columns should be treated as categories.

For example, you might have a column that contains the numbers 0,1 and 2, but know that the numbers actually mean "Smoker", "Non smoker" and "Unknown". In that case, by flagging the column as categorical you can ensure that the values are not used in numeric calculations, only to group data.

Scenario:

Experiments for local crowd sentiment models must combine local penalty detection data.

Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.

Note: Evaluate the changed in correlation between model error rate and centroid distance

In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.

References:

https://en.wikipedia.org/wiki/Nearest_centroid_classifier

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering

Step 1: Define a cross-entropy function activation

When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.

Step 2: Add cost functions for each target state.

Step 3: Evaluated the distance error metric.

References:

https://www.analytics vidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/

Box 1: import pytorch as deeplearninglib

Box 2: ..DistributedSampler(Sampler)..

DistributedSampler(Sampler):

Sampler that restricts data loading to a subset of the dataset.

It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.

Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.

Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10)

Scenario:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1 +/- 5%.

Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.

In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript."

Scenario:

Local penalty detection models must be written by using BrainScript.

References:

https://docs.microsoft.com/en-us/cogniti ve-toolkit/post-batch-normalization-statistics

Split Data partitions the rows of a dataset into two distinct sets.

The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.

Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.

Scenario:

Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.

The distribution of features across training and production data are not consistent

References:

https://docs.microsoft.co m/en-us/azure/machine-learning/studio-module-reference/split-data