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The one other problem you have because of this is you are stuck with just this solution and this may not be the best one. Yes, you can use regularization but still, what if some other better solution exists than this. Since, in boosted trees, you aim is to reduce your errors, each iteration of boosting tries to overfit on the examples which the last ...


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You can use tf.image.resize, as follows: (x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data() print(x_trian.shape) # (60000, 28, 28) # train set / data x_train = np.expand_dims(x_train, axis=-1) x_train = tf.image.resize(x_train, [32,32]) # if we want to resize print(x_train.shape) # (60000, 32, 32, 1)


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I would suggest going with the representative dataset because it considers as the snapshot of the real data. But for the sampling, you can try to implement stratified sampling, which can help for the imbalanced class issues. The classification process ( Decision Tree, Bayesian, Rule-based ) might work with this dataset.


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There is GoogleNews package for Python. It allows queries by data range, keyword, and language.


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This might help if what you're asking is related to merging models: Merging two different models in Keras


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Generally, without knowing the source of the data, we can't tell you much about the columns. But I assume the first two columns correspond to $x$ and $y$. The third is probably some meta-data? Maybe a cluster number? For an illustration I found this figure coming from this publication. Maybe that helps you imagine the data's shape. https://www.researchgate....


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First of all look at the shape of tensors that your tf.data.Dataset returns then try to set the input_shape of the first Dense layer like: model = keras.Sequential([ layers.Dense(520, activation='relu', input_shape=(1, 519)), layers.Dense(520, activation='relu'), layers.Dense(520, activation='relu'), layers.Dense(1) ]) or explicitly add ...


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With a search online, I couldn't find a satisfying answer, so I came up with a way. I tested and it gave pretty nice accuracy. This method is inspired by this paper. Similar to the approach in the paper, I first select the number of convolution kernels I want to use, and depending on the sampling rate of a data point, I apply convolution to it using the ...


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So this was my work around: Convert the list of strings that need to be translated into an indexed tuple with the first value being the index and the second value being the string: s = [(0, 'NEW'), (1, 'YOUTUBE'), (2, 'VIDEO'), (3, 'OUT'), (4, 'NOW:TOTTENHAM'), (5, 'NEWS'), (6, 'TRANSFER'), (7, 'WINDOW'), (8, 'UPDATE'), (9, '손흥민'), (10, 'Son'), (11, 'Award'...


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Essentially you want to pick a function that will give you the "size" of a matrix. The most obvious way I can think of is by choosing a matrix norm, which is a map $\lVert \cdot \rVert \colon \mathbb{R}^{k, k} \to [0, \infty)$ (or you could generalise to a complex $k \times k$ matrix if you wished). Your suggestion seems similar to computing $$S = \...


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The case described can be cast as imbalanced dataset problem, or rare events problem. Even more generally as highly non-uniform underlying distribution problem (which is an umbrella term for both cases). References: Machine Learning Tips: Handling Imbalanced Datasets Handling imbalanced datasets in machine learning


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I did a project with the CheXpert dataset which includes metadata and labels for uncertainty. I don't know if it's the right kind of metadata you are looking for. In general, medical datasets usually contain metadata. A similar one would be from the NIH. Otherwise, this is a decent index of high quality datasets.


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I found a solution that we should use the softmax activation function in the last layer. previously I used the sigmoid activation function


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