Search results for "Model Risk"

A Gentle Introduction to Model Selection for Machine Learning

A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on a given predictive modeling dataset. The challenge of applied machine learning, therefore, becomes how to choose among a range of different models that you can use for your problem. Naively, you might believe that model […]

Continue Reading 18
How to Develop Machine Learning Models for Multivariate Multi-Step Air Pollution Time Series Forecasting

How to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution

Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The EMC Data Science Global Hackathon dataset, or the ‘Air Quality […]

Continue Reading 117
Depiction of CNN Model for Accelerompter Data

Deep Learning Models for Human Activity Recognition

Human activity recognition, or HAR, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model. Recently, deep learning methods […]

Continue Reading 51
How to Use Small Experiments to Develop a Caption Generation Model in Keras

How to Use Small Experiments to Develop a Caption Generation Model in Keras

Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right […]

Continue Reading 85
Line Plot Showing Single Model Accuracy (blue dots) vs Accuracy of Ensembles of Varying Size With a Horizontal Voting Ensemble

How to Develop a Horizontal Voting Deep Learning Ensemble to Reduce Variance

Predictive modeling problems where the training dataset is small relative to the number of unlabeled examples are challenging. Neural networks can perform well on these types of problems, although they can suffer from high variance in model performance as measured on a training or hold-out validation datasets. This makes choosing which model to use as […]

Continue Reading 0
Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Machine learning model selection and configuration may be the biggest challenge in applied machine learning. Controlled experiments must be performed in order to discover what works best for a given classification or regression predictive modeling task. This can feel overwhelming given the large number of data preparation schemes, learning algorithms, and model hyperparameters that could […]

Continue Reading 2
A Gentle Introduction to Computational Learning Theory

A Gentle Introduction to Computational Learning Theory

Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it is a sub-field where having […]

Continue Reading 4