def build_model(hidden_layers=1, layer_size=30, learning_rate=3e-3): model = keras.models.Sequential() model.add(some layers) model.compile(loss='mse', optimizer=keras.optimizers.SGD(learning_rate)) return model model = keras.wrappers.scikit_learn.KerasRegressor(build_model) rsc = Randomize...
As a reaction to the demand for these types of sophisticated explanations, in the last years, we have witnessed the rise of a plethora of counterfactual explanation methods, each one focusing on some desirable properties for the returned counterfactual instances (Artelt and Hammer2019; Karimi et a...
Ant Species Prediction ModelModeling Biological Communities with Pandas and Sklearn Libraries Community · Eric Hilary Smith· 1 Variation · 0 Notebooks arrow_drop_up0more_horiz 🚀 Ultimate Mumbai House Price Predictor🏠 Predict Mumbai Real Estate Prices with 99.8% Accuracy Community · Anirudh Bag...
If you use a scikit-learn classifier, all cleanlab methods will work out-of-the-box. It’s also easy to use your favorite model from a non-scikit-learn package, just wrap your model into a Python class that inherits the sklearn.base.BaseEstimator:...
If you use a scikit-learn classifier, all cleanlab methods will work out-of-the-box. It’s also easy to use your favorite model from a non-scikit-learn package, just wrap your model into a Python class that inherits the sklearn.base.BaseEstimator:...
Tensorflow, caffe2, scikit-learn, mxnet, etc. If you use a scikit-learn classifier, allcleanlabmethods will work out-of-the-box. It’s also easy to use your favorite model from a non-scikit-learn package, just wrap your model into a Python class that inherits thesklearn.base.BaseEstimato...
Capable of handling large-scale data Insights: F1 macro-average will be used in place of accuracy or F1-weighted based on different considerations for precision-recall tradeoff F1 macro-average will compute the metric independently for each class and then take the average (hence treating all classe...
Scaling Vision Transformers: ‘As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well on few-shot learning, for example, attaining 84.86% top-1 accuracy on ImageNe...