Search results for "Machine Learning"

XGBoost Plot of Single Decision Tree

How to Visualize Gradient Boosting Decision Trees With XGBoost in Python

Plotting individual decision trees can provide insight into the gradient boosting process for a given dataset. In this tutorial you will discover how you can plot individual decision trees from a trained gradient boosting model using XGBoost in Python. Let’s get started. Update Mar/2018: Added alternate link to download the dataset as the original appears […]

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Text Generation With LSTM Recurrent Neural Networks in Python with Keras

Text Generation With LSTM Recurrent Neural Networks in Python with Keras

Recurrent neural networks can also be used as generative models. This means that in addition to being used for predictive models (making predictions) they can learn the sequences of a problem and then generate entirely new plausible sequences for the problem domain. Generative models like this are useful not only to study how well a […]

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Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

A powerful and popular recurrent neural network is the long short-term model network or LSTM. It is widely used because the architecture overcomes the vanishing and exposing gradient problem that plagues all recurrent neural networks, allowing very large and very deep networks to be created. Like other recurrent neural networks, LSTM networks maintain state, and […]

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Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras

Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras

Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence. What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input symbols and may require […]

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ROC Curve Explaination

Assessing and Comparing Classifier Performance with ROC Curves

The most commonly reported measure of classifier performance is accuracy: the percent of correct classifications obtained. This metric has the advantage of being easy to understand and makes comparison of the performance of different classifiers trivial, but it ignores many of the factors which should be taken into account when honestly assessing the performance of […]

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