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## python What should I use if I have millions of

2018 9 17&ensp·&enspWhat should I use if I have millions of categories for a sklearn predictive model? word2vec does this so it is feasible. So one option is to just do it. Another option is to use the hashing trick with a fixed length input as wide as where you predict the final category for each category. In python, this is easier than it seems. You can

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## Online Learning Perceptron MLWave

A perceptron is a supervised classifier. It learn by first making a prediction: Is the dotproduct over or below the threshold? Online Learning Perceptron in Python. Hashing trick. The vectorizing hashing trick originated with Vowpal Wabbit John Langford. This trick sets number ofg connections to the perceptron to a fixed size.

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## ShallowLearn · PyPI

Using the hashing trick together with partial_fitX, y yields a powerful online text classifier see Online learning. It is possible to load pre trained word vectors at initialization, passing a Gensim Word2Vec or a ShallowLearn LabeledWord2Vec instance the latter is retrievable from a GensimFastText model by the attribute classifier .

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## Quickly Building a Face Recognizer

2017 7 12&ensp·&enspWe then use a special trick called perceptual hashing to remove duplicate facial features. Last but not least, install the Python Algorithmia client usingmand

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## Online Learning Guide with Text Classification using

2018 1 17&ensp·&enspVowpal Wabbit is so incredibly fast in part due to the hashing trick. With many features and a small sized hash, collisions i.e. when we have the same hash for 2 different features start occurring. These collisions may influence the results.

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## tf.contrib.estimator.RNNClassifier TensorFlow

2018 8 16&ensp·&ensppredict_keys: list of str, name of the keys to predict. It is used if the EstimatorSpec.predictions is a dict . If predict_keys is used then rest of the predictions will be filtered from the dictionary.

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## GitHub alialavia/TwitterNews: An automatic news

The system is entirely build using python and we use the NLTK and Scikit Learn libraries to do the preprocessing. For each tweet we extract the text, tokenize it, stem it and turn it into a sparse feature vector by using the hashing trick.

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## Machine Learning Apache Spark to the rescue

2015 8 21&ensp·&enspThe HashingTF uses feature hashing or well known as hashing trick for converting the text to feature vector. What hashing does it convert text elements to numerical elements within a given range.

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## ATCS Learning and Prediction Tsinghua University

2016 6 6&ensp·&enspATCS Learning and Prediction Theory and Practice. 2016 Spring. Lecturer: kernel trick, clustering, Adaboost, gradient boosting, random forest, basic graphical models including naive bayes, CRF, LDA Python is the default programming language we will use in the course.

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## API reference seqlearn 0.1 documentation

2014 4 12&ensp·&enspDataset loading utilities¶ seqlearn.datasets.load_conllf, features, n_features=65536, split=False¶ Load CoNLL file, extract features on the tokens and vectorize them. The ConLL file format is a line oriented text format that describes sequences in a space separated format, separating the sequences with blank lines.

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## Newest 'machine learning' Questions Page 3 Cross

2018 10 6&ensp·&enspMy classifier has a multivariate normal distribution with distinct covariance matrices for each class and with equal values across the diagnose, ie $\Sigma_i$=$\sigma_{_i}^2\cdot I$. If we add a machine learning posterior discriminant analysis multivariate normal multivariate distribution

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## A Gentle Introduction to the Bag of Words Model

2017 10 9&ensp·&enspThe bag of words model is a way of representing text data when modeling text with machine learning algorithms. The bag of words model is simple to understand and implement and has seen great success in problems such as language modeling and document classification.

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## DocumentVector Hashing KNIME

This workflow demonstrates how to use the Document Vector Hashing node to execute the Sentiment Analysis example in a streaming fashion.

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## sklearn.linear_model.SGDClassifier scikit learn

2018 10 14&ensp·&enspThe log loss gives logistic regression, a probabilistic classifier. modified_huber is another smooth loss that brings tolerance to outliers as well as probability estimates. squared_hinge is like hinge but is quadratically penalized. perceptron is the linear loss used by the perceptron algorithm.

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## Outline of PhD Course of Machine Learning in CMU CSDN

2013 11 20&ensp·&enspHashing trick 7. Kernels Application jet engine failure detection Support Vector Novelty Detection Density estimation qq_35478624：Python

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## pyspark.mllib package PySpark 2.3.2 documentation

2018 9 26&ensp·&enspClears the threshold so that predict will output raw prediction scores. It is used for binary classification only. pyspark.mllib.feature module¶ Python package for feature in MLlib. Maps a sequence of terms to their term frequencies using the hashing trick. Note. The terms must be hashable can not be dict/set/list.

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## Feature Extraction and Transformation RDD based

2018 10 16&ensp·&enspOur implementation of term frequency utilizes the hashing trick. A raw feature is mapped into an index term by applying a hash function. A raw feature is mapped into an index term by applying a

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## SVM using Scikit Learn in Python Learn OpenCV

2018 7 27&ensp·&enspIf you have downloaded the code, here are the steps for building a binary classifier. Prepare data: We read the data from the files points_class_0.txt and points_class_1.txt.These files simply have x and y coordinates of points one per line.

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## python How to vectorize bigrams with the hashing

2018 8 2&ensp·&enspHow to vectorize bigrams with the hashing trick in scikit learn? Ask Question. up vote 3 down vote Here's one way to use the CountVectorizer and the naive bayes classifier. The following example is from Ask a new question if you need help with Unicode handling in Python, it's a tricky subject in its own right. Fred Foo Nov 3 '14 at

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## API Reference scikit learn 0.20.0 documentation

2018 10 17&ensp·&enspAPI Reference ¶ This is the class and function reference of scikit learn. Implements feature hashing, aka the hashing trick. The one vs the rest meta classifier also implements a predict_proba method, so long as such a method is implemented by the base classifier. This method returns probabilities of class membership in both the single

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## Natural Language Processing Coursera

2017 11 16&ensp·&enspYou will learn how to predict next words given some previous words. This task is called language modeling and it is used for suggests in search, machine translation, chat bots, etc. Also you will learn how to predict a sequence of tags for a sequence of words.

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## Using categorical data in machine learning with

Method 3: Feature hashing a.k.a the hashing trick Feature hashing is a very cool technique to represent categories in a one hotg style as a sparse matrix but with a much lower dimensions.

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## Newest 'python' Questions Cross Validated Stack

2018 10 8&ensp·&enspPython is a general purpose programming language designed for ease of use. It ismonly used platform for machine learning. Use this tag for any *on topic* question that a involves Python either as a critical part of the question or expected answer,

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## sklearn.externals.joblib.load Python Example

The following are 50 code examples for showing how to use sklearn.externals.joblib.load.They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don't like.

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## Click Through Rate Prediction Kaggle

Predict whether a mobile ad will be clicked

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## Introducing one of the best hacks in machine

2017 12 30&ensp·&enspThinking in more general terms, the hashing trick allows you to use variable size feature vectors with standard learning algorithms regression, random forests, feed

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## Machine Learning Natural Language Processing

2018 10 12&ensp·&enspThis text vectorizer implementation uses the hashing trick to find the token string name to feature integer index mapping. This strategy has several advantages: It is very low memory scalable to large datasets as there is no need to store a vocabulary dictionary in memory.

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## Data Exploration, Machine Learning and AI

2018 9 15&ensp·&enspThe Hashing Trick With R Published Feb 21, 2015 Yelp, httr and a Romantic Trip Across the United States, One Florist at a Time Published Jan 14, 2015 Quantifying the Spread: Measuring Strength and Direction of Predictors with the Summary Function Published Dec 27, 2014

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## Machine Learning with Python, scikit learn and

2018 10 5&ensp·&enspPredict the values of continuous variables using linear regression and K Nearest Neighbors to classify documents and images using logistic regression and support vector machines Requirements Familiarity with Machine Learning fundamentals will be useful.

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## Simple Methods to deal with Categorical Variables in

2015 11 27&ensp·&enspIn python, library sklearn requires features in numerical arrays. Look at the below snapshot. I have applied random forest using sklearn library on titanic data set only two features sex and pclass are taken as independent variables.

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## python Make predictions from a saved trained

2018 7 23&ensp·&enspI wrote a classifier for Tweets in Python which then I saved it in .pkl format on disk, so I can run it again and again without the need to train it each time. This is the code: import pandas impo

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## GitHub giacbrd/ShallowLearn: A collection of

Using the hashing trick together with partial_fitX, y yields a powerful online text classifier see Online learning. It is possible to load pre trained word vectors at initialization, passing a Gensim Word2Vec or a ShallowLearn LabeledWord2Vec instance the latter is retrievable from a GensimFastText model by the attribute classifier .

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## scikit learn sample try out with my classifier and data

combine two different classifier result in scikit learn pythonbine the classification of two classifiers that output class assignment probabilities via the predict_proba method you can average possibly with some weights the probabilies and take the argmax over the average predicted class probabilities for the final prediction.

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## tf.contrib.timeseries.predict_continuation_input_fn

2018 8 16&ensp·&enspAn Estimator input_fn for running predict after evaluate. If the call to evaluate we are making predictions based on had a batch_size greater than one, predictions will start after each of these windows i.e. will have the same batch dimension.

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## Python: Real World Machine Learning by Luca

Read Python: Real World Machine Learning by Luca MassXinhai, Alberto Boschetti, and Prateek Joshi by Luca MassXinhai, Alberto Boschetti, and Prateek Joshi by Luca MassXinhai, Alberto Boschetti, Prateek Joshi, John Hearty, Bastiaan Sjardin for free with a 30 day free trial.

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## How to train a SVM classifier from text examples

2015 6 20&ensp·&enspWith the training examples, and these features, you can now train an SVM classifier. One possible problem is that it can lead to a large number of features, and classifiers tend to perform poorly in such high dimensional spaces, due to 'the curse of dimensionality'.

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## Python CTOLib

2018 9 12&ensp·&enspVoting: soft voting predict probability and average over all individual learners often works better than hard voting NB classifier works quite well in real world applications,

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## How to Prepare Text Data for Deep Learning with Keras

2017 10 2&ensp·&enspInstead, the function is a wrapper for the hashing_trick function described in the next section. The function returns an integer encoded version of the document. The use of a hash function means that there may be collisions and not all words will be assigned unique integer values.

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## scikit learn : Support Vector Machines SVM II 2018

2018 10 15&ensp·&enspThis is where the so called kerneles into play. The kernel trick avoids the explicit mapping that is needed to get linear learning algorithms to

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## machine learning How can I do classification with

2018 9 27&ensp·&enspHowever, my categorical variable is city so it could happen that the person I am trying to predict has a new city that my classifier has never seen. I am wondering if there is a way to do the classification in these terms or if I should do the training again considering this new categorical data.

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