An Introduction to Statistical Learning

James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert

doi:10.1007/978-1-0716-1418-1

Cited by 815 publications

(794 citation statements)

References 0 publications

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“…In the Building the models step, several machine learning models are built [27,28] and for each bacteria the model with the best performance is used for evaluation:…”

Section: Model Trainingmentioning

confidence: 99%

Modelling Bathing Water Quality Using Official Monitoring Data

Džal

Kosović

Mastelić

et al. 2021

Water

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Predictive models of bathing water quality are a useful support to traditional monitoring and provide timely and adequate information for the protection of public health. When developing models, it is critical to select an appropriate model type and appropriate metrics to reduce errors so that the predicted outcome is reliable. It is usually necessary to conduct intensive sampling to collect a sufficient amount of data. This paper presents the process of developing a predictive model in Kaštela Bay (Adriatic Sea) using only data from regular (official) bathing water quality monitoring collected during five bathing seasons. The predictive modelling process, which included data preprocessing, model training, and model tuning, showed no silver bullet model and selected two model types that met the specified requirements: a neural network (ANN) for Escherichia coli and a random forest (RF) for intestinal enterococci. The different model types are probably the result of the different persistence of two indicator bacteria to the effects of marine environmental factors and consequently the different die-off rates. By combining these two models, the bathing water samples were classified with acceptable performances, an informedness of 71.7%, an F-score of 47.1%, and an overall accuracy of 80.6%.

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“…In the Building the models step, several machine learning models are built [27,28] and for each bacteria the model with the best performance is used for evaluation:…”

Section: Model Trainingmentioning

confidence: 99%

Modelling Bathing Water Quality Using Official Monitoring Data

Džal

Kosović

Mastelić

et al. 2021

Water

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“…Both penalties shrink the coefficient estimates toward zero, relative to the usual (weighted) least-squares estimates, and the more so the larger λ is. As λ increases, the shrinkage of the coefficient estimates reduces the variance of the predictions, at the expense of an increase in bias (James et al, 2021). Selecting a good value for λ is therefore critical for finding a good balance between variance and bias, and cross-validation is commonly used for this purpose.…”

Section: Model-assisted Estimatorsmentioning

confidence: 99%

A Comparison of Model-Assisted Estimators, With and Without Data-Driven Transformations of Auxiliary Variables, With Application to Forest Inventory

Ekström

Nilsson

2021

Front. For. Glob. Change

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Forest information is requested at many levels and for many purposes. Sampling-based national forest inventories (NFIs) can provide reliable estimates on national and regional levels. By combining expensive field plot data with different sources of remotely sensed information, from airplanes and/or satellite platforms, the precision in estimators of forest variables can be improved. This paper focuses on the design-based model-assisted approach to using NFI data together with remotely sensed data to estimate forest variables for small areas, where the variables studied are total growing stock volume, volume of Norway spruce (Picea abies), and volume of broad-leaved trees. Remote sensing variables may be highly correlated with one another and some may have poor predictive ability for target forest variables, and therefore model selection and/or coefficient shrinkage may be appropriate to improve the efficiency of model-assisted estimators of forest variables. For this purpose, one can use modern shrinkage estimators based on lasso, ridge, and elastic net regression methods. In a simulation study using real NFI data, Sentinel 2 remote-sensing data, and a national airborne laser scanning (ALS) campaign, we show that shrinkage estimators offer advantages over the (weighted) ordinary least-squares (OLS) estimator in a model-assisted setting. For example, for a sample size n of about 900 and with 72 auxiliary variables, the RMSE was up to 41% larger when based on OLS. We propose a data-driven method for finding suitable transformations of auxiliary variables, and show that it can improve estimators of forest variables. For example, when estimating volume of Norway spruce, using a smaller expert selection of auxiliary variables, transformations reduced the RMSE by up to 10%. The overall best results in terms of RMSE were obtained using shrinkage estimators and a larger set of 72 auxiliary variables. However, for this larger set of variables, the use of transformations yielded at most small improvements of RMSE, and at worst large increases of RMSE, except in combination with ridge and elastic net regression.

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“…The most successful kinds of machine learning algorithms are those that automate decision-making processes by generalizing from known examples. This is known as supervised learning [32]. Of the many machine learning methods, some are less flexible, or more restrictive, being the shallow models simpler in the sense that they can produce just a relatively small range of shapes to estimate.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…Prediction is made by aggregating (majority vote for classification or averaging for regression) the calculations of the set. Random Forest generally exhibits a substantial improvement in performance over the single tree and provides a low error rate with an exceptional noise resistance [32]. The n_estimators parameter indicates the number of trees to grow in the RF model.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Vigoya¹,

Fernández²,

Carneiro³

et al. 2021

Electronics

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With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

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An Introduction to Statistical Learning

Cited by 815 publications

References 0 publications

Modelling Bathing Water Quality Using Official Monitoring Data

Modelling Bathing Water Quality Using Official Monitoring Data

A Comparison of Model-Assisted Estimators, With and Without Data-Driven Transformations of Auxiliary Variables, With Application to Forest Inventory

IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

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