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Model Selection & Training For Machine Learning
Synaptiq.ai's Chief Data Scientist, Dr. Tim Oates, talks about the importance of model selection and model training in...
Model Selection & Training
Model Selection and Training represents the scientific process of training a computer to answer questions through the use of data.
Why does Model Selection & Training matter?
To allow computers to undertake tasks formerly requiring human insight, companies must have the ability to select and train models in the context of technical and business needs. This allows them to derive maximum value from data, automate processes, and extract data-driven insights.
Model Selection
Knowledge and expertise to effectively select machine learning models in the context of technical and business needs.
My organization has a data science team that can effectively select an appropriate machine learning model in the context of the technical and business needs.
My organization effectively organizes and tracks experiment iterations when developing models.
My organization has an established and standardized framework for efficiently selecting and training machine learning models.
Model Optimization
Knowledge and expertise to effectively optimize machine learning model performance and minimize model error.
My organization effectively evaluates and optimizes machine learning models through hyperparameter tuning to optimize model performance.
Hyperparameter tuning is the process for optimizing a set of learning variables, or hyperparameters, for a machine learning model. Tuning is performed using optimization search methods, such as grid, gradient-based, or Bayesian, to minimize model error.
My organization effectively applies strategies to manage and prevent underfitting and overfitting in machine learning models.
Understanding model fit is important for evaluating model accuracy. Underfitting occurs when the model performs poorly on both the training and validation data. Overfitting occurs when the model performs well on the training data, but not the validation data.
Feature Engineering
Practices and procedures for extracting business-relevant information from raw data and preparing it to support machine learning model development.
My organization understands the importance and carefully evaluates the quantity and quality of data when training models.
My organization applies data augmentation techniques to enhance limited or low quality datasets.
Data augmentation is used to enhance or modify an existing dataset in order to create additional "new" data for model training. For example, image data can be augmented by flipping, translating, cropping, or brightening the existing images to expand the training dataset.
My organization effectively uses feature engineering techniques to prepare data for machine learning modeling, such as imputation, encoding, binning, and automated feature discovery.
Feature engineering is the process for designing and extracting business-relevant information from raw data. Identifying appropriate features improves the quality of the predictions to drive business insights. Example techniques include imputation for managing missing values in datasets, encoding for transforming categorical data into numerical data, binning for categorizing numerical data to reduce noise, or automating the discovery of features within datasets.
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