Practical Machine Learning with Python

Sarkar, Debabrata; Bali, Raghav; Sharma, Tushar

doi:10.1007/978-1-4842-3207-1

Cited by 101 publications

(60 citation statements)

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“…This latter difference is referred to as the bias of the technique [1]. By balancing a model's complexity one achieves an optimal trade-off between bias and variance of a model [24,27]. In fact, the authors of [27] make a recommendation for small sample sizes of using k-fold cross validation because of the good variance and bias properties for only minor additional computational costs due to the rather small sample sizes.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Another approach to determine feature importance is a model-agnostic version called model reliance, where feature importance is indicated by the amount of increase of model error, for example measured by the AUC or any other performance measure, by fitting a model after permuting the features [29,30]. We will use the model-agnostic approach [24] as implemented in Skater [31]. Skater measures the mean absolute value of the change in predictions given a perturbation of a certain feature.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Machine learning is a statistical approach that aims at automating analytical model fitting for data analysis, for instance finding clusters/structures in data or generating data-based predictive decision tools. For very comprehensive introductions to machine learning the reader is referred to [23,24]. We use so-called supervised learning, where the training data also includes the true solutions.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…The model performance metrics as well as the AUC indicate very high performance of the trained random forest models, whereas a slight decline can be observed with increasing orientation angle. Figure 9 shows the feature importance for the three scenarios for the random forest model which was computed using the model agnostic approach [24] as implemented in Skater [31]. For perfectly aligned grains (0°misorientation) there are essentially two most important features, the absolute value of the vertical position of the grain in the magnet (zposition) and the distance of the grain from the center of the magnet.…”

Section: Microstructure Machine Learning Analysismentioning

confidence: 99%

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Magnetic microstructure machine learning analysis

et al. 2018

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We use a machine learning approach to identify the importance of microstructure characteristics in causing magnetization reversal in ideally structured large-grained Nd 2 Fe 14 B permanent magnets. The embedded Stoner-Wohlfarth method is used as a reduced order model for determining local switching field maps which guide the data-driven learning procedure. The predictor model is a random forest classifier which we validate by comparing with full micromagnetic simulations in the case of small granular test structures. In the course of the machine learning microstructure analysis the most important features explaining magnetization reversal were found to be the misorientation and the position of the grain within the magnet. The lowest switching fields occur near the top and bottom edges of the magnet. While the dependence of the local switching field on the grain orientation is known from theory, the influence of the position of the grain on the local coercive field strength is less obvious. As a direct result of our findings of the machine learning analysis we show that edge hardening via Dy-diffusion leads to higher coercive fields.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Microstructure Machine Learning Analysismentioning

confidence: 99%

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Magnetic microstructure machine learning analysis

et al. 2018

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“…CNNs have also integrated with other algorithms, such as multilayer perceptrons [30] and support vector machines [31]. Many studies have reported that CNNs have contributed to an accuracy improvement of land cover classification, with the overall accuracy ranging from 81% to 93%, depending on the sensor type, spatial resolution of input images, and target classes [18,19,21,27,[29][30][31][32][33].Feature engineering is defined as the process of transforming raw data into features for better representation of the given problem, which can result in an improvement of the model accuracy on unseen data [34]. Good features are a contributing factor in model performance since machine learning algorithms are problem specific and dependent on their domains.…”

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confidence: 99%

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

et al. 2020

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This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas—Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea—were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%–95 % for both Concord and Lake Tapps and 80%–84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited.

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