Classification is a type of supervised learning where the outcome (target) is the category to which the data belongs. It’s the task of approximating a mapping function (f) from input variables (X) to discrete output variables (Y). Some examples include:
- Email filtering: it helps detect whether an email is spam or not.
- Medical imaging: it helps identify whether a tumor is malignant or benign.
- Credit scoring: it assists in determining whether a customer is a default risk.
A few algorithms used for Classification include Logistic Regression, Decision Trees, Random Forest, Naïve Bayes and so on.
Azure Machine Learning
Before we delve into classification in Azure, let’s understand what Azure machine learning is. Azure Machine Learning is a cloud-based predictive analytics service that provides a streamlined experience for all data scientist skill levels. It supports both code-first and low-code experiences.
Azure Machine Learning designer allows you to create, test, and deploy ML models graphically, without any code. However, if you are comfortable with coding, Azure Machine Learning provides SDKs for Python that can be used in Jupyter notebooks or IDE.
Classification Scenarios with Azure Machine Learning
Now let’s discuss some of the classification scenarios with Azure Machine Learning:
Automated Machine Learning
To facilitate the work of data scientists, Azure Machine Learning proposes an Automatic ML. It automates the process of creating ML models. The following is an example of a classification scenario where AutoML is used.
Assuming we want to predict customer churn. We have the historical data indicating whether a customer left or is still with us. Azure Machine Learning provides a simple AutoML interface where you can specify the dataset, the target variable, and the type of model (classification). You just have to press “Run experiment” and let Azure handle the rest!
Azure ML Designer
This is a drag-and-drop tool to build, test, and deploy models which follows a very intuitive and visual process. For instance, predicting the quality of wine based on different chemical properties can be a classification task. You load data, pre-process if necessary, divide into train/test, use a classification model, score the model, and finally evaluate the model. Each of these steps is done by simply dragging and dropping appropriate modules.
Azure Custom Vision
This is another instance where Azure implements classification. It’s an image classification service that easily customizes your own computer vision models. Let’s consider a scenario where a fruit juice company wants to sort fruits based on their types. Here, Custom Vision can classify images into “apple”, “banana”, “pear”, etc.
Model Evaluation
Finally, remember to select the right evaluation metrics for your classification model. For binary classification, such as detecting whether an email is spam or not, metrics like Accuracy, Precision, Recall are useful. For multi-class classification, metrics like Multiclass Log Loss and Mean Per-Class Error play a crucial role.
Conclusion
To put it in a nutshell, Azure offers a variety of services that support various classification ML models. Given your scenario, whether you are aiming for low-code or code-first or image classification, Azure has a service to offer! Remember, the essence of a good ML model is selecting the right model with appropriate pre-processing, justified by the right evaluation measure.
Practice Test
In machine learning, classification is used to predict a categorical outcome.
- A) True
- B) False
Answer: A) True
Explanation: In machine learning, classification algorithms are used to predict/identify which category a new data point belongs to out of predefined set of categories.
Microsoft Azure does not provide tools for implementing machine learning classification scenarios.
- A) True
- B) False
Answer: B) False
Explanation: Microsoft Azure offers a number of services such as Azure Machine Learning, Cognitive Services, etc., which allow implementing classification scenarios.
Which of the following are examples of classification problems in machine learning? (Multiple select)
- A) Predicting if an email is spam or not
- B) Predicting housing prices
- C) Identifying the breed of a dog in an image
- D) Predicting tomorrow’s weather conditions
Answer: A) Predicting if an email is spam or not, C) Identifying the breed of a dog in an image, D) Predicting tomorrow’s weather conditions
Explanation: These are classification problems as they involve predicting a categorical outcome. Predicting housing prices is a regression problem since it involves predicting a continuous outcome.
A binary classification problem involves:
- A) Just two possible categories
- B) Multiple categories
- C) Continuous values
- D) None of the above
Answer: A) Just two possible categories
Explanation: In binary classification, there are two possible outcomes. For instance, predicting if an email is spam or not is a binary classification problem.
In machine learning, classification is a type of supervised learning.
- A) True
- B) False
Answer: A) True
Explanation: Classification is a type of supervised learning as the model is trained on labeled data, i.e., we have the input features (X) and the output (y).
Which of the following can be used for text classification in Azure AI? (Multiple Select)
- A) Language Understanding (LUIS)
- B) Text Analytics API
- C) QnA Maker
- D) All of the above
Answer: A) Language Understanding (LUIS), B) Text Analytics API
Explanation: Both Language Understanding and Text Analytics API can analyze and classify text data. QnA Maker is mainly used for creating a conversational question and answer layer over the data.
The process of predicting a continuous value is called classification.
- A) True
- B) False
Answer: B) False
Explanation: The process of predicting a continuous value is called regression, while classification deals with predicting categorical values.
Multi-class classification involves:
- A) Just two possible categories
- B) More than two categories
- C) Both A and B
- D) None of the above
Answer: B) More than two categories
Explanation: Multi-class classification involves predicting one of more than two classes.
All machine learning classification models require pre-processing of data.
- A) True
- B) False
Answer: A) True
Explanation: Preprocessing is a crucial step in machine learning models to convert or scale the data into a format that is more digestible for the models, thus improving their performance.
Image classification can be performed using which service in Microsoft Azure AI?
- A) Form Recognizer
- B) QnA Maker
- C) Custom Vision Service
- D) Personalizer
Answer: C) Custom Vision Service
Explanation: Custom Vision Service in Azure is used to build and refine customized models that are able to automatically classify images.
Interview Questions
What is the primary goal of classification in machine learning?
The main goal of classification in machine learning is to predict categorical class labels of new instances, based on past observations.
Which type of machine learning algorithm would be suitable for identifying whether an email is spam or not?
A classification algorithm would be suitable for identifying whether an email is spam or not.
What is the principle of Binary Classification in Machine Learning?
Binary Classification in machine learning is a type of classification task that outputs one of two mutually exclusive classes. For example, a model might classify emails as either “spam” or “not spam”.
Can you use the Azure Machine Learning designer for classification scenarios?
Yes, the Azure Machine Learning designer supports both binary and multiclass classification scenarios.
What is multiclass classification in machine learning?
In multiclass classification, the classification task is to categorize instances into one of three or more classes.
Is logistic regression a suitable machine learning model for classification tasks?
Yes, logistic regression is commonly used for binary classification tasks.
Which machine learning service in Azure can be used to build, train, and deploy machine learning models?
Azure Machine Learning is the service in Azure that can be used to build, train, and deploy machine learning models.
In Azure Machine Learning, what is Automated Machine Learning(automl) used for?
Automated Machine Learning (automl) in Azure Machine Learning is used for building and tuning a machine learning model using automation, which simplifies the machine learning process.
Which Azure service utilizes machine learning for interpreting and acting on user’s voice commands?
Azure Speech Service implements machine learning for interpreting and acting on a user’s voice commands.
What is a confusion matrix in classification tasks?
A confusion matrix is a table layout that allows visualization of the performance of an algorithm in classification tasks. It indicates the accuracy of a classification model by comparing the actual and predicted classes.
Can classification algorithms handle numerical data?
Yes, classification algorithms can handle numerical data by segmenting it into categories or groups.
What Azure tool allows you to visually build, train and deploy machine learning models?
Azure Machine Learning designer allows you to visually build, train, and deploy machine learning models.
What is one-vs-all strategy for multiclass classification problems?
The one-vs-all strategy involves training a single classifier per class, with the samples of that class as positive samples and all other samples as negatives.
Is it possible to use the Azure Machine Learning SDK to create a classification model?
Yes, you can use the Azure Machine Learning SDK to create a classification model.
When should you retrain a classification model in Azure Machine Learning?
You should consider retraining a classification model in Azure Machine Learning when your model’s performance drops, possibly because the data it was trained on is no longer representative of the current situation.
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