prediction

问题 I prefer to use caret when fitting models because of its relative speed and preprocessing capabilities. However, I'm slightly confused on how it makes predictions. When comparing predictions made directly from the train object and predictions made from the extracted final model, I'm seeing very different numbers. The predictions from the train object appear to be more accurate. library(caret) library(ranger) x1 <- rnorm(100) x2 <- rbeta(100, 1, 1) y <- 2*x1 + x2 + 5*x1*x2 data <- data.frame

问题 Using the pima indians diabetes dataset I'm trying to build an accurate model using Keras. I've written the following code: # Visualize training history from keras import callbacks from keras.layers import Dropout tb = callbacks.TensorBoard(log_dir='/.logs', histogram_freq=10, batch_size=32, write_graph=True, write_grads=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None) # Visualize training history from keras.models import Sequential from

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 I currently have these data points of date vs cumulative sum. I want to predict the cumulative sum for future dates using python. What prediction method should I use? My dates series are in this format: ['2020-01-20', '2020-01-24', '2020-01-26', '2020-01-27', '2020-01-30', '2020-01-31'] dtype='datetime64[ns]' I tried spline but seems like spline can't handle date-time series I tried Exponential Smoothing for time series forecasting but the result is incorrect. I don't understand what predict

问题 I currently have these data points of date vs cumulative sum. I want to predict the cumulative sum for future dates using python. What prediction method should I use? My dates series are in this format: ['2020-01-20', '2020-01-24', '2020-01-26', '2020-01-27', '2020-01-30', '2020-01-31'] dtype='datetime64[ns]' I tried spline but seems like spline can't handle date-time series I tried Exponential Smoothing for time series forecasting but the result is incorrect. I don't understand what predict

问题 Using R I would like to compare the RMSE (root mean square error) from two prediction models. The first model uses estimates from 1966 to 2000 to predict 2001 and then uses estimates from 1966 to 2001 to predict 2002 and so on up to 2015. The second model uses estimates from 1991 to 2000 to predict 2001 and then uses estimates from 1992 to 2001 to predict 2002 and so on up to 2015. This problem has me really stumped and I truly appreciate any help. DF <- data.frame(YEAR=1966:2015, TEMP=rnorm

问题 Using R I would like to compare the RMSE (root mean square error) from two prediction models. The first model uses estimates from 1966 to 2000 to predict 2001 and then uses estimates from 1966 to 2001 to predict 2002 and so on up to 2015. The second model uses estimates from 1991 to 2000 to predict 2001 and then uses estimates from 1992 to 2001 to predict 2002 and so on up to 2015. This problem has me really stumped and I truly appreciate any help. DF <- data.frame(YEAR=1966:2015, TEMP=rnorm