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model-evaluation

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lime

thomasp85 / lime

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error in text_classification_explanation

IrinaMax
IrinaMax commented Dec 6, 2019

I have issue with model explamation lime() try demo in lime/demo/text_classification_explanation.R it game me error:

`results <- lime(sentences_to_explain, bst, get.features.matrix, keep_word_position = false)(cases = sentences_to_explain, n_labels = 1, n_features = 5)

Error in eval(lhs, parent, parent) : attempt to apply non-function
`
Can you please suggest? Thanks.

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maykulkarni / Machine-Learning-Notebooks

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Machine Learning notebooks for refreshing concepts.

python machine-learning natural-language-processing reinforcement-learning deep-learning machine-learning-algorithms neural-networks deep-learning-algorithms dimensionality-reduction python-machine-learning data-processing regression-models deep-learning-tutorial data-science-notebook model-evaluation classification-trees clustering-methods machine-learning-tutorials
  • Updated Oct 31, 2018
  • Jupyter Notebook

Cloud-CV / GSoC-Ideas

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Analytics Dashboard for EvalAI Admin, Challenge hosts & Participants

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Sanji515
Sanji515 commented Feb 5, 2020

Project Title: Analytics Dashboard for EvalAI Admin, Challenge hosts & Participants

Description:

As the number of compute-intensive challenges on EvalAI are increasing, we want to focus on improving the performance of our services. As a first step, we will focus on monitoring and measuring all key metrics of our services. Insights from these will allow us to efficiently utilize our

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Analytics Dashboard for EvalAI Users GSoC-2020
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PacktWorkshops / The-Deep-Learning-with-Keras-Workshop

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An Interactive Approach to Understanding Deep Learning with Keras

machine-learning tensorflow scikit-learn keras cross-validation regression classification artificial-neural-networks logistic-regression regularization support-vector-machine vectors decision-trees hyperparameter-tuning model-evaluation magnetic-resonance-imaging k-means-clustering model-tuning scalars linear-transformation
  • Updated Jun 3, 2020
  • Jupyter Notebook

mansipatel2508 / Image-Forgery-Detection-CNN-vs-Transfer-Learning-Comparison

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The binary classification problem focused on first IEEE Image forensics challenge-phase 1, to predict the given image is pristine or manipulated/edited/fake. Comparing CNN & Transfer Learning models for the problem and boosting the performance by feature extraction

ai kernel robotics image-processing data-visualization feature-extraction pickle pylab opencv-python cnn-keras binary-classification model-evaluation binary-image image-preprocessing image-handle imread image-classfication computer-vision-opencv image-data-generator
  • Updated Aug 10, 2019
  • Jupyter Notebook

mithulmanoj / Classification---imbalanced-dataset---KNN-XGBoost-NaiveBayes

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Classification of an imbalanced dataset using SMOTE oversampling technique and ML Algorithms - KNN , XGBoost and Naive Bayes classifier

exploratory-data-analysis cross-validation data-visualization feature-selection naive-bayes-classifier xgboost hyperparameter-optimization feature-engineering knn model-evaluation model-building feature-importance label-encoding smotetomek numerical-encoding ordinal-encoding correlation-map imbalanced-dataset
  • Updated May 9, 2020
  • Jupyter Notebook

liyijin-data-PM / Applied-Machine-Learning-in-Python-Coursera

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A wide variety of supervised and unsupervised machine learning methods using the scikit-learn library

scikit-learn cross-validation regularization confusion-matrix decision-trees ridge-regression support-vector-machines roc-curve k-nearest-neighbours model-evaluation model-complexity lasso-regression supervised-machine-learning roc-auc gridsearchcv precision-recall-curve
  • Updated Nov 6, 2019
  • Jupyter Notebook

Ashwanikumarkashyap / RNN-based-new-lyrics-generation-fromScratch

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In the project, the aim is to generate new song lyrics based on the artist’s previously released song’s context and style. We have chosen a Kaggle dataset of over 57,000 songs, having over 650 artists. The dataset contains artist name, song name, a link of the song for reference & lyrics of that song. We tend to create an RNN character-level language model on the mentioned dataset. Using model evaluation techniques, the model is checked for its accuracy and is parameter optimized. The trained model will predict the next character based on the context of the previous sequence and will generate new lyrics based on an artist’s style.

artists machine-learning recurrent-neural-networks neural-networks supervised-learning rnn neurons model-evaluation kaggle-dataset song-lyrics cross-entropy-loss lyrics-generation
  • Updated May 17, 2020
  • Python

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