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So, I understand the question is asking what data should you use, when given new data. You might want to provide more context to your question as this might seem like a loose answer. The way you go forward with this depends on a couple of factors: Is the initial dataset ($D_1$) have the same features/columns as the new dataset ($D_2$), such that you can ...


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There is this dataset and if it doesnt match, maybe you could contact the authors at Brussels University for more information. https://data.world/raghu543/credit-card-fraud-data The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 ...


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I am attaching below a screen shot of calculations performed to explain the definition of variance in Wikipedia. Create 5-models from 5-different training datasets (similar to but not exactly, 5-fold cross-validation). That is prepare 5-models from differing training datasets. To each model, feed the same test data. Our test-data has two observations. Below ...


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Downloading BAIR Robot Pushing Small: import tensorflow_datasets as tfds # to prevent ResourceExhaustedError # https://github.com/tensorflow/datasets/issues/1441#issuecomment-581660890 import resource low, high = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit(resource.RLIMIT_NOFILE, (high, high)) builder = tfds.builder('...


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Your issue might occur if you didn't tell that the second row in the table should also be considered a header line. To address this, try to reset the header at the beginning. import pandas as pd df = pd.read_csv('YOUR/FILE/DIRECOTRY.csv', skiprows=1) // ignore the 2nd row (0-indexed) df.rename(columns={0:'Index'}, inplace=True) // optional df.groupby(['...


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It depends on the problem you are working on. If number of categorical variables is very large, it is better to use label encoding. But the label encoding should be meaningful i.e. the categories which are close to each other should get similar labels. Let's say you are creating a model where you have a feature Month. But there is a periodicity in your ...


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I am well aware that this is an old post, however for anybody with similar problems I would like to mention AlphaVantage. AlphaVantage (https://www.alphavantage.co/) offers a free stock data API with a lot of request flags. All you need is an account and you're good to go. I have personally used AlphaVantage and have not had any problems.


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It would depend on the use cases - read vs write vs analytics etc. Nonetheless, you may want to explore Hadoop if not done already.


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With Keras, you could use the functional API, to estimate a model with two outputs („multioutput“). Simply train the model on two outputs like: # Outputs out1 = Dense(1)(x) out2 = Dense(1)(x) # Compile/fit the model model = Model(inputs=Input_1, outputs=[out1,out2]) model.compile(optimizer = "rmsprop", loss = 'mse') # Add actual data here in the ...


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The problem with a pre-defined validation set is that it can lead to overfitting more easily: the primary purpose of a validation set is to detect overfitting and if you keep tuning your hyperparameters for your model using a fixed validation set every time you train, then your model hyperparameters may be overfitting to that specific validation set.


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If you have already split your training and validation sets into separate directories then there is no need to technically do the splitting in your code. However, the problem with a pre-defined validation set is that it can lead to overfitting more easily: the primary purpose of a validation set is to detect overfitting and if you keep tuning your ...


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The specific ordering is only metadata as it is not explicitly in the data itself, and the index doesn't always represent order. If you have an additional row with the order in then it would be two dimensional. But, for example, you wouldn't be able to distinguish between ascending and descending order by looking at the index itself. v(t) is velocity by time ...


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You are the domain expert on this, but these are the two practical approaches, I have utilized depending on the use case and effect on performance: 1. Downsample: Let say, randomly choose 5,000 out of 80,000 records. To maintain the same proportion of classes as in the population, go for stratified random sampling. 2. Reduce the precision of measurements (i....


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You can use PCA (Principal Component Analysis) that is a technique of dimensionality reduction. Basically you will change the basis of the data, you will not drop any columns, minimizing information loss. So, for sure you will lose some information, but it's up to you decide how much information lose, depending on the number of principal components you will ...


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This is specified in the dataset description in paperswithcode.com (emphasis mine): IG-3.5B-17k is an internal Facebook AI Research dataset for training image classification models. It consists of hashtags for up to 3.5 billion public Instagram images. So the dataset is not public. In the paper, however, the authors argue that the images and their hashtags ...


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Ok, I guess here how it should be read. A sample could be in multiple categories. First, you look into a sample. Below is an example (at row 12) and the mail content: Content Cat_1_level_1 Cat_1_level_2 Cat_1_weight Cat_2_level_1 Cat_2_level_2 Cat_2_weight Jennifer, Thank-you for stepping in on this and guiding the process! ---------------------- Forwarded ...


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