A Gentle Introduction to Probability Scoring Methods in Python

Last Updated on December 31, 2019 How to Score Probability Predictions in Python andDevelop an Intuition for Different Metrics. Predicting probabilities instead of class labels for a classification problem can provide additional nuance and uncertainty for the predictions. The added nuance allows more sophisticated metrics to be used to interpret and evaluate the predicted probabilities. In general, methods for the evaluation of the accuracy of predicted probabilities are referred to as scoring rules or scoring functions. In this tutorial, you […]

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Probabilistic Forecasting Model to Predict Air Pollution Days

Last Updated on August 28, 2020 Air pollution is characterized by the concentration of ground ozone. From meteorological measurements, such as wind speed and temperature, it is possible to forecast whether the ground ozone will be at a sufficiently high level tomorrow to issue a public air pollution warning. This is the basis behind a standard machine learning dataset used for time series classification dataset, called simply the “ozone prediction problem“. This dataset describes meteorological observations over seven years in […]

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Indoor Movement Time Series Classification with Machine Learning Algorithms

Last Updated on August 28, 2020 Indoor movement prediction involves using wireless sensor strength data to predict the location and motion of subjects within a building. It is a challenging problem as there is no direct analytical model to translate the variable length traces of signal strength data from multiple sensors into user behavior. The ‘indoor user movement‘ dataset is a standard and freely available time series classification problem. In this tutorial, you will discover the indoor movement prediction time […]

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A Gentle Introduction to a Standard Human Activity Recognition Problem

Last Updated on August 5, 2019 Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-size […]

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How to Develop a Framework to Spot-Check Machine Learning Algorithms in Python

Last Updated on August 28, 2020 Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of algorithms rapidly and provide a rough first-cut result. This first cut result may be used to get an idea […]

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How to Model Human Activity From Smartphone Data

Last Updated on August 5, 2019 Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized […]

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Evaluate Machine Learning Algorithms for Human Activity Recognition

Last Updated on August 28, 2020 Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field. Recently, deep learning methods such as recurrent neural networks […]

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1D Convolutional Neural Network Models for Human Activity Recognition

Last Updated on August 28, 2020 Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field. Recently, deep learning methods such as recurrent neural networks […]

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LSTMs for Human Activity Recognition Time Series Classification

Last Updated on August 28, 2020 Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires strong expertise in the field. Recently, deep learning methods such as recurrent neural networks […]

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Deep Learning Models for Human Activity Recognition

Last Updated on August 5, 2019 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 such as convolutional neural networks and recurrent neural networks have shown capable and even achieve state-of-the-art results by automatically […]

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