Microsoft DP-100 Practice Test - Questions Answers, Page 5

One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner; the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model

B: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.

Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.

Three different regularizer instances are provided; they are:

L1: Sum of the absolute weights.

L2: Sum of the squared weights.

L1L2: Sum of the absolute and the squared weights.

D: Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.

By avoiding training all nodes on all training data, dropout decreases overfitting.

Reference:

https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/

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

Accuracy, Precision, Recall, F1 score, and AUC are metrics for evaluating classification models.

Note: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error are OK for the linear regression model.

Reference:

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

A: Try Penalized Models

You can use the same algorithms but give them a different perspective on the problem.

Penalized classification imposes an additional cost on the model for making classification mistakes on the minority class during training. These penalties can bias the model to pay more attention to the minority class.

B: You can change the dataset that you use to build your predictive model to have more balanced data.

This change is called sampling your dataset and there are two main methods that you can use to even-up the classes:

Consider testing under-sampling when you have an a lot data (tens- or hundreds of thousands of instances or more)

Consider testing over-sampling when you don't have a lot of data (tens of thousands of records or less)

D: Try Generate Synthetic Samples

A simple way to generate synthetic samples is to randomly sample the attributes from instances in the minority class.

Reference:

https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

Business managers in your organization want to see the importance of the features in the model.

You need to print out the model features and their relative importance in an output that looks similar to the following.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Explanation:

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

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-contrib-interpret/azureml.contrib.interpret.explanation.explanation_client.explanationclient?view=azure-ml-py

Box 1: StandardScaler

The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.

Example:

All features are now on the same scale relative to one another.

Box 2: Min Max Scaler

Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.

Box 3: Normalizer

References:

http://benalexkeen.com/feature-scaling-with-scikit-learn/

Step 1: Use the Filter Based Feature Selection module

Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.

The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.

Step 2: Build a counting transform

A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.

Step 3: Test the hypothesis using t-Test

References:

https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selection

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform

Box 1: Boosted Decision Tree Regression

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

Box 2:

Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.

References:

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

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

In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.

Note: In statistics and machine learning, the bias–variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.

References:

https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/