Yellowbrick: Visualizing the Scikit-Learn Model Selection Process

Bengfort, Benjamin; Bilbro, Rebecca

doi:10.21105/joss.01075

Cited by 111 publications

(66 citation statements)

References 6 publications

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“…To select the appropriate number of clusters the criterion of maximizing the silhouette coefficient was adopted. All calculations were made using own computer codes written in Python using the Scikit-learn [ 94 ] and the Yellowbrick [ 95 ] libraries.…”

Section: Resultsmentioning

confidence: 99%

“…The vertical dotted line in the elbow method plot indicates the optimal number of clusters which was determined by using the knee point detection algorithm. The knee point detection algorithm finds the point of maximum curvature, which in a well-behaved clustering problem also represents the pivot of the elbow curve (see Bengfort and Bilbro [ 95 ] and Satopaa et al [ 99 ]). We found that for all the investigated periods the elbow method points towards the same clustering as the method based on the silhouette coefficients.…”

Section: Resultsmentioning

confidence: 99%

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COVID-19 pandemic and stability of stock market—A sectoral approach

2021

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The COVID-19 pandemic seems to be the most important phenomenon observed from March 2020 in virtually all countries of the world. The necessity to prevent the spread of COVID-19 and keep health care systems efficient resulted in the forced, drastic limitation of economic activity. Many service sectors were hit particularly hard with this but industry and agriculture were also affected. In particular, the pandemic substantially influenced financial markets and we can observe that some markets or instruments vary in stability since they have been affected in the different degree. In the paper, we present the problem of stability of stock markets during the COVID-19 pandemic. Due to the low number of works related to CEE countries during the pandemic, we analyze the Warsaw Stock Exchange, which is one of the most important markets in the CEE. Our main goal was to find how various industries represented by stock market indices have reacted to the COVID-19 shock and consequently which sectors turned out to keep stability and remained resistant to the pandemic. In our investigation, we use two clustering methods: the K-means and the Ward techniques with the criterion of maximizing the silhouette coefficient and six indicators describing stability in terms of profitability, volume, overbought/oversold conditions and volatility. The results of the research present that during the pandemic it was possible to identify 5 clusters of sector indices in the short term and 4 in the medium term. We found that the composition of the clusters is quite stable over time and that none of the obtained clusters can be univocally considered the most or the least stable taking into account all the analyzed indicators. However, we showed that the obtained clusters have different stability origins, i.e. they vary from each other in terms of the investigated indicators of stability.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

COVID-19 pandemic and stability of stock market—A sectoral approach

2021

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“…The number of clusters is determined with the elbow method. A quick running of the Yellowbrick [25] implementation of the elbow method suggests that the number of clusters at 20 gives a balance between more variation and over-fitting.…”

Section: Parameter Settingsmentioning

confidence: 99%

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

Liao

et al. 2020

Applied Sciences

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Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset.

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“…DNN models were built with supervised machine learning in a Python-3 environment 19 . Python libraries used for model building are Tensorflow 20 , Keras 21 , Pandas 22 , NumPy 23 , Scikit-learn 24 , Matplotlib 25 , Seaborn 26 and Yellowbricks 27 . Web-app was built with Streamlit python package (https://www.streamlit.io/, 2020; Online; accessed 30-11-2020).…”

Section: Dataset Preparation and Preprocessingmentioning

confidence: 99%

AIDrugApp: Artificial Intelligence-based Web-App for Virtual Screening of Inhibitors against SARS-COV-2

Karade

Karade²

2020

Preprint

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Currently, there is no effective cure for SARS-COVID-19 diseases. The identification of novel therapeutic targets and drug-like compounds is required for the development of anti-COVID-19 drugs. Virtual screening is currently the most significant component for identifying drug-like molecules from large datasets for drug design and development. But there are no effective easily available and user-friendly applications for virtual screening of drug leads against SARS-COV-2. Therefore, we have developed a user-friendly web-app named “AIDrugApp” for the virtual screening of inhibitor molecules against SARS-CoV-2. AIDrugApp is a novel open-access, deep learning AI-based inhibitory activity prediction and data statistics visualization platform. Users can predict the inhibitory activities (Active/ Inactive) and pIC-50 values of new compounds against SARS-CoV-2 replicase polyprotein, 3CLpro and human angiotensin-converting enzymes. It is also useful for virtual screening of chemical features of molecules towards SARS-COVID-19 clinical trial bioactivities. This paper presents the development and architecture of AIDrugApp. We also present two case studies where large sets of molecules were screened by our app. Screened molecules were analyzed further by molecular docking and ADME analysis to identify the potential drug candidates. Web-App URL: <a href="https://sars-covid-app.herokuapp.com/">https://sars-covid-app.herokuapp.com/</a>

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Yellowbrick: Visualizing the Scikit-Learn Model Selection Process

Cited by 111 publications

References 6 publications

COVID-19 pandemic and stability of stock market—A sectoral approach

COVID-19 pandemic and stability of stock market—A sectoral approach

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

AIDrugApp: Artificial Intelligence-based Web-App for Virtual Screening of Inhibitors against SARS-COV-2

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