AppServers, Grid, SOA, JEE, etc.

Click on image to see full size. Read the comments for description.

One comment “Building Mashine Learning Model.”

1. admin Says:
   November 2, 2023 в 4:21 pm

   Building a Machine Learning Model: A Visual Breakdown

   Building a machine learning model is an iterative process that generally involves the following steps:
   —————————————
   1. Initial data collection: Gather the raw data that will be used to train the model from reliable sources.
   2. Data understanding: Explore the data to identify data quality issues, understand distributions and relationships between variables.
   3. Data preparation: Clean the data to handle missing values, duplicates, errors, etc. Transform or engineer features as needed.
   4. Data splitting: Split the preprocessed data into training, validation and test sets. The training set is used to fit the models, validation set to tune hyperparameters, and test set to evaluate final model performance.
   5. Model training: Train multiple machine learning models on the training data, trying different algorithms and hyperparameters.
   6. Model evaluation: Evaluate the trained models on the validation set to select the best model. Metrics depend on the task – for classification problems, accuracy, AUC-ROC, etc. are used.
   7. Hyperparameter tuning: Tune hyperparameters of the best model to improve validation performance.
   8. Final evaluation: Evaluate the final model on the test set to get an unbiased estimate of its real-world performance.
   9. Model deployment: Deploy the final model to production for making predictions on new data. Monitor its performance to detect potential drift.
   ——————————————-
   The process focuses on analyzing, preparing, and splitting the data properly for robust model development and evaluation. Iterating on modeling, evaluation and tuning is key for maximizing predictive performance.

Add comment

You need to register , for comment of article.