We all know that, there are weather prediction models and case studies. But I don't understand the reason, why people trust them rather than weather forecast on TV. I mean, what is advantages of Machine Learning weather prediction over classic weather forecast?
What are the major differences between Kaggle notebook and Google Colab notebook? To work on a dataset my first step is to start a Kaggle notebook but then I cant help thinking what could be the advantage of using Colab notebook instead. I know few differences, correct me if I'm mistaken about any: Kaggle has a console and Colab doesn't (but I still don't know what to do with the console). Kaggle notebook allows collaboration with other users on Kaggle's …
I am fitting a recurrent neural network (RNN) on some non-stationary time series data. I know that, in the case of linear models, it is common practice to difference the series in order to make them stationary and remove trends. However, is this also the case when fitting neural networks? I have found several tutorials and examples where stationarity is not even checked and others which recommend first differencing if the data is non-stationary. What is the best way to …
I have the following data in table where I want to calculate the average time between 1st and 2nd call. I know how to get the average, but I have a though time to figure out how to subtract the 2nd from 1st attempt since it is in the same column and I am more familiar to subtracting things between columns.
I have a dataset of the following form: System A Rating System B Rating 4.5 5 3 4 5 3 etc. etc. I have 155 such data points gathered using a survey. Which statistical test should I use to show statistical significance if one system has significantly better ratings than the other? Thanks.
I would like to compare the scores of two multi-class classifiers. I have calculated the MAUC score for each of the algorithms, and now I want to see whether there is a statistical difference between the results. From what I have read so far, the McNemar test seems to be a good alternative, however, I am not sure how exactly to use it. In this article, there is an example o how to use McNemar's test to compare the accuracy …
What are the differences and the similarities between data classification (using dedicated distance-based methods) and data clustering (which has certain defined methods such as k-means) Is data classification a sub-topic of data clustering ?
From Wikipedia: Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. How to distinguish them? What are the exact differences?
I have come across a David Silver's slide which contains both the terms "bootstrapping" and "sampling". Is there any realistic example which helps me to understand the concepts better.
From what I understand, differencing is necessary to remove the trend and seasonality of a time series. So I assumed it basically does the same thing as signal.detrend from the scipy library. But I tried differencing and then, separately, used signal.detrend and my time series looked completely different. Original: Differencing: Imported libraries: The x axis represents months and the y axis is sales. The colours on the first two charts just represent three different years.
I want to use CNN transfer learning to track a tennis ball from TV broadcasts of tennis matches. I used VGG annotating tool annotation tool link (use version 1 of the tool for compatibility with matterport code) and have about 200 frames annotated with ball location and x,y coordinates given by the tool for the bounding circle. Like this: But, the ball is occluded by the bottom player's body or the net tape at times and at others practically invisible …
While reading some articles about Bayesian Networks, I came across many occurrences of Belief Networks. Do both of these terms mean the same thing or is there any difference between Bayesian Networks and Belief Networks?
I am trying to find the working of dataframe.columns.difference() but couldn't find a satisfactory explanation about it. Can anyone explain the working of this method in detail?
I am trying to fit and test LSTM on a numeric series(like stock prices). But it seems that I always get a lag in predicted graph(Blue) with respect to real graph(red). Does anyone know why this happened? (I searched and realized this is a problem for others too). I use Keras.sequential.LSTM library.