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We train and compare several classifiers using these descriptors on several benchmark datasets, and the random forest method is selected to build an efficient predictor of protein-RNA interactions.
We trained several classifiers using the datasets under study.
The selection of this particular size was determined experimentally, training several classifiers using different image sizes and choosing the size that produced the smallest classification error in the average case, which was 32 × 32.
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Briefly, it calculates several different possible classifiers using different shrinkage thresholds (i.e. different number of genes) and finds the best threshold from crossvalidation.
In our SVM method, several classifiers encoded using the BFE scheme registered better prediction performance when measured by the same metrics; P10P10'-Bayes P10P10'-Bayes.86 and FPR of 0.14, as with as P14P10'-Bayes, P8P8'-Bayes and P6P6'-Bayes wiTPRTPRs of 0.86 and FPRs of 0.14.
Using all the data several classifiers were built using two machine learning techniques namely Support Vector Machines (SVM) and Neural Networks (NN).
Now, we introduce several classifiers that use infection betweenness centrality and other node features.
The second layer of phishGILLNET (phishGILLNET2) employs classifier ensemble technique AdaBoost and topic probabilities as features to build a robust classifier using several base learners.
These models arrive at features that are often tuned to suite a classifier using several machine learning strategies at selection or criteria assessment stage.
SVM classifiers used the RBF kernel and default settings to emulate the SVM classifiers used in19.
All classifiers used 10 features.
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CEO of Professional Science Editing for Scientists @ prosciediting.com