prediction

问题 I have a simple data set for which I applied a simple linear regression model. Now I would like to use fixed effects to make a better prediction on the model. I know that I could also consider making dummy variables, but my real dataset consist of more years and has more variables so I would like to avoid making dummies. My data and code is similar to this: data <- read.table(header = TRUE, stringsAsFactors = FALSE, text="CompanyNumber ResponseVariable Year ExplanatoryVariable1

问题 I'm using the NuGet package Microsoft.Cognitive.CustomVision.Prediction version 1.2.0 . I created 1 trial project and trained it with a few images. Now when I try to call the API for a prediction using the PredicionEndpoint , the system throws an exception: Microsoft.Rest.HttpOperationException . When I debug the code and inspect the exception, it says: {"Operation returned an invalid status code 'NotFound'"} This is my code: var attachmentStream = await httpClient.GetStreamAsync(imageUrl);

问题 I have a binomial GLM in R, with several predictors that are both continuous and categorical. The response variable is "Presence", which is binary (0/1). Length is a continuous variable, while all others are categorical. I am trying to plot predictions for each of the variables in the final model, particularly for "length", but I'm having difficulties. My data are the following: MyData<-structure(list(site = structure(c(3L, 1L, 3L, 2L, 1L, 4L, 3L, 4L, 1L, 2L, 4L, 5L, 5L, 1L, 4L, 3L, 2L, 4L,

问题 I am trying to use the XGBoost implementation by DMLC on Spark-1.6.1. I am able to train my data with XGBoost but facing difficulties in prediction. I actually want to do prediction in the way it can be done in Apache Spark mllib libraries, that helps with calculation of training error,precision, recall, specificity etc. I am posting the codes below, also the error I am getting. I used this xgboost4j-spark-0.5-jar-with-dependencies.jar in spark-shell to start. import org.apache.spark.mllib

问题 I am trying to build a forecasting model using ARIMAX in R and require some guidance on how covariates are handled in xreg argument. I understand that, auto.arima function takes care of differencing of covariates while fitting the model (from training period data) and I also don't need to difference the covariates for generating forecasts for test period (future values). However, while fitting the model using Arima() in R with custom (p, d, q) and (P, D, Q)[m] values with d or D greater than

问题 I have a few data sets from similar periods of time. It's a presentation of people at that day, the period being about a year. The data hasn't been gathered in regular intervals, it is rather quite random: 15-30 entries for each year, from 5 different years. The graph drawn from the data for each year looks roughly like this: Graph made with matplotlib. I have the data in datetime.datetime, int format. Is it possible to predict, in any sensible way, how things will turn out in the future? My

问题 Example: if (almost_always_false_condition) { // do something } Is there a way to suggest compiler that in 99% condition will be false. The condition calculation takes ~60 cycles to be checked, and it cannot be calculated at compile time by compiler itself. (gcc 4.3) 回答1: If you want to suggest to GCC that a condition is likely to have a given value as a hint for arranging code flow, you should use __builtin_expect( ) : if (__builtin_expect(almost_always_false_condition,0)) { // do something

问题 I trying to understand linear regression... here is script that I tried to understand: ''' A linear regression learning algorithm example using TensorFlow library. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' from __future__ import print_function import tensorflow as tf from numpy import * import numpy import matplotlib.pyplot as plt rng = numpy.random # Parameters learning_rate = 0.0001 training_epochs = 1000 display_step = 50 # Training Data

问题 Overview I know that one can get the percentages of each prediction in a trained WEKA model through the GUI and command line options as conveniently explained and demonstrated in the documentation article "Making predictions". Predictions I know that there are three ways documented to get these predictions: command line GUI Java code/using the WEKA API, which I was able to do in the answer to "Get risk predictions in WEKA using own Java code" this fourth one requires a generated WEKA .MODEL

问题 I am curious what are the methods / approaches to overcome the "cold start" problem where when a new user or an item enters the system, due to lack of info about this new entity, making recommendation is a problem. I can think of doing some prediction based recommendation (like gender, nationality and so on). 回答1: Maybe there are times you just shouldn't make a recommendation? "Insufficient data" should qualify as one of those times. I just don't see how prediction recommendations based on