Introduction to Scikit-Learn

Paper, David

doi:10.1007/978-1-4842-5373-1_1

Cited by 62 publications

(53 citation statements)

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“…The features were standardized to have zero mean and unit variance, which is a widely used scaling approach as algorithms such as Radial SVM assume features are centered around zero [ 36 , 40 ]. For each feature, the mean (µ) and the standard deviation (σ) were extracted from the raw training feature values.…”

Section: Methodsmentioning

confidence: 99%

Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study

Shaukat-Jali¹,

Zalk²,

Boyle³

2021

JMIR Form Res

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Background Subclinical (ie, threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection. Objective This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA). Methods Young adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index. Results With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity. Conclusions The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.

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Section: Methodsmentioning

confidence: 99%

Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study

Shaukat-Jali¹,

Zalk²,

Boyle³

2021

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…We performed a sampling technique that takes the number of classes in each sample used, in an attempt to address class imbalance and we oversampled the underrepresented classes. To this end, the com-pute_sample_weight function from sklearn is used to calculate the weights of each sample by considering the number of different classes in each sample (i.e., class diversity) [31,32]. Calculated sample weights are then given as a sample to Pytorch DataLoader.…”

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confidence: 99%

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

et al. 2022

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Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.

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“…When it comes to the data set used for simulation, our simulator runs experiments over open-source data sets from sklearn package [3]. Among all data sets from that package, we consider two representative sets, boston [27] and diabetes [18], since they are designed for running the linear regression model.…”

Section: Data Description and Benchmark Selectionmentioning

confidence: 99%

Outlier Backdoor Attack Prevention Through Energy-Based Learning

Gao

Qiu

2023

Preprint

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The improvement of data collection and data storage tech- niques enables it quite easier to obtain large amount of data. Accord- ingly, multiple big-data oriented machine learning become quite popular in terms of decision making. However, the quality of machine learning tools’ output is highly dependent on the cleanness of the input data. As for backdoor attackers, they have strong motivation to pollute the data set to negatively influence the final output. In response to this concern, this paper proposes to leverage energy-based learning to prevent back- door attacks. Specifically, our algorithm cleans the data set iteratively to rule out several possible polluted points before running the machine learning model. The experiments take linear regression as the example and show that out algorithm is fast to implement and can improve the accuracy of final output by 60% on average.

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Introduction to Scikit-Learn

Cited by 62 publications

References 0 publications

Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study

Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

Outlier Backdoor Attack Prevention Through Energy-Based Learning

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