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How can you apply a supervised ML algorithm to a regression problem?

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If you want to use machine learning (ML) to predict a continuous outcome, such as the price of a house or the demand for a product, you need to apply a supervised ML algorithm to a regression problem. Regression is a type of ML task that aims to find the relationship between one or more input variables (features) and a numeric output variable (target). In this article, you will learn how to apply a supervised ML algorithm to a regression problem in six steps.

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1 Choose a regression algorithm

The first step is to choose a suitable regression algorithm for your problem. There are many types of regression algorithms, such as linear regression, logistic regression, decision tree regression, support vector regression, and neural network regression. Each algorithm has its own advantages and disadvantages, such as complexity, accuracy, interpretability, and scalability. You should consider the characteristics of your data, such as the number and type of features, the distribution and range of the target, and the presence of outliers and noise, when choosing a regression algorithm.

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Sanjay Kumar MBA,MS,PhD
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Selecting an appropriate regression algorithm is the initial step in building a regression model. Various options include linear regression, logistic regression, decision tree regression, support vector regression, and neural network regression, each with unique attributes like complexity, accuracy, interpretability, and scalability. The choice should align with your data's characteristics, including feature count and type, target distribution and range, and the presence of outliers or noise. Careful consideration of these factors ensures a suitable algorithm for your regression problem.

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Ranganath Venkataraman

Digital Transformation through AI and ML | Decarbonization and Oil&Gas | Project Management and Consulting
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When stuck on choosing a first algorithm, try to start simple and add complexity as needed to achieve target model performance results.

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2 Prepare the data

The next step is to prepare the data for the regression algorithm. This involves cleaning, transforming, and splitting the data. Cleaning the data means removing or imputing missing values, handling duplicates and errors, and checking for inconsistencies. Transforming the data means applying techniques such as scaling, normalization, encoding, and feature engineering to make the data more suitable for the algorithm. Splitting the data means dividing the data into training, validation, and test sets, which are used to train, tune, and evaluate the algorithm respectively.

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Sanjay Kumar MBA,MS,PhD
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Data preparation for regression involves several key steps. First, you must clean the data by addressing missing values, duplicates, and errors while ensuring consistency. Next, you transform the data through techniques like scaling, normalization, encoding, and feature engineering to make it suitable for the chosen algorithm. Finally, the data is split into training, validation, and test sets, used for training, tuning, and evaluating the regression model, respectively. These steps are crucial for ensuring the data is ready for effective modeling.

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3 Train the model

The third step is to train the model using the training set. This means fitting the regression algorithm to the data and finding the optimal parameters that minimize the error between the predicted and actual values of the target. Depending on the algorithm, you may need to specify some hyperparameters, such as the learning rate, the regularization term, or the number of iterations, that control the behavior of the algorithm. You can use various methods, such as grid search, random search, or Bayesian optimization, to find the best combination of hyperparameters.

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4 Validate the model

The fourth step is to validate the model using the validation set. This means testing the performance of the model on unseen data and comparing it with the performance on the training set. You can use various metrics, such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), or coefficient of determination (R-squared), to measure the accuracy of the model. You can also use graphical methods, such as residual plots, scatter plots, or learning curves, to visualize the fit and error of the model.

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5 Evaluate the model

The fifth step is to evaluate the model using the test set. This means assessing the generalization ability of the model on new data and verifying that it does not overfit or underfit the data. You can use the same metrics and methods as in the validation step, but you should not use the test set to tune or modify the model. The test set should only be used once, at the end of the process, to get an unbiased estimate of the model's performance.

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Ranganath Venkataraman

Digital Transformation through AI and ML | Decarbonization and Oil&Gas | Project Management and Consulting
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Selecting the right metric matters from a business perspective and it could ultimately influence the final model selected. For example.. R2 values focus on explaining variation in results while MAE focuses only in errors.

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6 Deploy the model

The final step is to deploy the model to production, where it can be used to make predictions on real-world data. This involves saving, exporting, and integrating the model with the application or system that requires it. You may also need to monitor, update, and maintain the model, depending on the changing conditions and requirements of the problem. You can use various tools and frameworks, such as TensorFlow, PyTorch, Scikit-learn, or MLflow, to facilitate the deployment and management of the model.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ranganath Venkataraman

Digital Transformation through AI and ML | Decarbonization and Oil&Gas | Project Management and Consulting
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AutoML can help you in selecting the right regression model; techniques like TPOT can be focused on regression. They will complete everything from data processing to model selection and validation for a given metric and dataset

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Vishnu Nandakumar

Dev @Kotak | IIT Madras | Open Source Contributor | 2 x AWS |
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MLOps is one other feature that we need to consider in any machine learning solution. MLOps basically orchestrates and automates the complete lifecycle of ML solutions. From data preparation to model monitoring practising MLOps automates and manages to reduce the manual effort. There are many popular frameworks available to implement MLOps and the common one are KubeFlow, MLFlow, Weights & Biases etc. Apart from readily available frameworks we can also leverage Dagster, Airflow which are orchestation tools for managing the steps in any process and can be extended to MLOps. Cloud platforms like AWS, GCP and Azure etc have their respective MLOps services (Sagemaker, Vertex AI etc)

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How can you apply a supervised ML algorithm to a regression problem?

1

2

3

4

5

6

7

1 Choose a regression algorithm

2 Prepare the data

3 Train the model

4 Validate the model

5 Evaluate the model

6 Deploy the model

7 Here’s what else to consider

Machine Learning

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How can you apply a supervised ML algorithm to a regression problem?

1

2

3

4

5

6

7

1 Choose a regression algorithm

2 Prepare the data

3 Train the model

4 Validate the model

5 Evaluate the model

6 Deploy the model

7 Here’s what else to consider

Machine Learning

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