Abstract Keywords Introduction Data preparation TF model TF-IDF model Add new features TF model tf_idf model n-gram model Conclusion: References Abstract Unlike the classical programming languages that are very slow and even sometimes fail to load very large data sets since they use only a single core, Apache Spark ...

Introduction Data preparation ordered logistic regression (logit) Ordinal logistic rgeression (probit) CART model Ordinal Random forst model. Continuation Ratio Model Compare models Conclusion Session information Introduction This tutorial aims to explore the most popular models used to predict an ordered response variable. We will use the heart disease data uploaded ...

Introduction data preparation Logistic regression Random forest Introduction H2O is an open source distributed scalable framework used to train machine learning and deep learning models as well as data analysis. It can handle large data sets, with ease of use, by creating a cluster from the available nodes. Fortunately, ...

Introduction Bayesian optimization Acquisition functions Data preparation Random forest model The true distribution of the hyperparameters random search bayesian optimization UCB bayesian optimization PI bayesian optimization EI Contrast the results deep learning model Random search Bayesian optimization UCB Bayesian optimization PI Bayesian optimization EI Contrast the results Conclusion Session info ...

Introduction Data preparation ARIMA model RNN model Reshape the time series Model architecture Model training Prediction results comparison Conclusion Further reading Introduction The classical methods for predicting univariate time series are ARIMA models (under linearity assumption and provided that the non stationarity is of type DS) that use the autocorrelation ...

Introduction Data Preparation Prediction Similarity based on distance The similarity based on the number of nearest neighbours Conclusion Further reading Introduction This model is an approximate version of knn model which is difficult to be implemented with large data set. In contrast to knn model that looks for the exact ...

Introduction Data preparation Training the model: Model Evaluation Prediction Conclusion Introduction In this article we will make use of the convolutional neural network, the most widely deep learning method used for image classification, object detection,..etc1. For more detail about how it works please click here. We are going be ...

Introduction Data preparation Classical linear regression model Bayesian regression Bayesian inferences PD and P-value Introduction For statistical inferences we have tow general approaches or frameworks: Frequentist approach in which the data sampled from the population is considered as random and the population parameter values, known as null hypothesis, as fixed (...

Introduction: Data preparation Data partition Poisson model Quasi poisson model Negative binomial model Hurdle model hurdle model with poisson distribution. hurdle model with negative binomial distribution. Zero inflated model Zero inflated model with poisson distribution Zero inflated model with negative binomial distribution Conclusion: Furhter reading: Introduction: When we deal with ...

Introduction Data preparation Data visualization Data partition Model training Fine tune the hyperparameters Conclusion: Introduction Decision tree1 is a model that recursively splits the input space into regions and defines local model for each resulted region. However, fitting decision tree model to complex data would not yield to accurate prediction ...

Introduction Data preparation Data partition train the model Evaluate the model Fine tune the model: Conclusion Introduction Naive bayes model based on a strong assumption that the features are conditionally independent given the class label. Since this assumption is rarely when it is true, this model termed as naive. However, ...

Introduction Data preparation Data partition train the model prediction and confusion matrix altering the link function Introduction In this paper we will fit a logistic regression model to the heart disease data uploaded from kaggle website. For the data preparation we will follow the same steps as we did in ...

Introduction Classification Data partition Train the model Prediction and confusion matrix Fine tuning the model Comparison between knn and svm model Regression Introduction In this paper we will explore the k nearest neighbors model using two data sets, the first is Tiatanic data to which we will fit this model ...

Introduction Installing sparklyr Installing spark Connecting to spark Importing data Manipulating data Disconnecting saving data Example of modeling in spark Streaming Introduction The programming language R has very powerful tools and functions to do almost every thing we want to do, such as wrangling , visualizing, modeling…etc. However, R such ...