I’m new to Machine Learning and I’m a bit confused about how data is being read for the training/testing process. Assuming my data works with date and I want the model to read the later dates first before getting to the newer dates, the data is saved in the form of earliest date on line 1 and line n has
Tag: machine-learning
xlearn predictions error give a different mse than output by the function
the xlearn predict function gives a different mse than what you get by looking at the predictions and calculating it yourself. Here is code to do this; you can run it by cloning the xlearn repository and copying the below code in demo/regression/house_price in the repository If you save it as min_eg.py, run it (after installing xlearn) as python min_eg.py
Python Numpy: int arrays can be converted to a scalar index
Please help me to get out of this error, maybe, it’s duplicate but I could not set it for my code. ERROR dataset Answer I think you might want to select your X columns slightly differently, e.g.
Keras model.evaluate accuracy stuck at 50 percent while using ImageDataGenerator
I am trying to find the accuracy of my saved Keras model using model.evaluate. I have loaded in my model using this: I have a CSV file with two columns, one for the filename of an image and one for the label. Here is a sample: I have loaded this CSV into a pandas dataframe and fed it into an
Variability/randomness of Support Vector Machine model scores in Python’s scikitlearn
I am testing several ML classification models, in this case Support Vector Machines. I have basic knowledge about the SVM algorithm and how it works. I am using the built-in breast cancer dataset from scikit learn. Using the code below: When printing the scores as in: When I run this code, I get certain scores per different regularization parameter C.
How to add code lines when calling a function depending on an iterator on Python
I am trying to test many ML models using keras.models.Sequential. My idea is that once I have an iterator that looks like [num_layers, num_units_per_layers], for example [(1, 64),(2, (64,128))], to create a script using a kind of for loop running the iterator to be able to create a keras sequential model with the number of layers and units in each
InvalidArgumentError: StringToNumberOp could not correctly convert string
I am trying to extract the labels from a file path of the form: The labels are 26, 0 and 3 in the file name first I create a list dataset: then I define a function that reads the image and gets the labels and use .map() on list_ds when I print some one of the labels as a sanity
Scikit-learn: Confused between coefficient of X0 and intercept
I have an extra column in my train/test set for feature/X which is just 1, this is supposed to be the coefficient for Xo, which is never in the dataset. It is mentioned to be θo in the equation; Now coming to the intercept, as a model parameter, I always knew this to be θ0. So I am a little
Split Pandas Dataframe With Equal Amount of Rows for each Column Value
This is for a machine learning project. I have a CSV file which I have read in as a Pandas dataframe. The CSV looks like this: I have decreased the sample size and equalized the data, so that I have a dataframe with 60,000 rows; 30,000 rows with label 1 and label 0. I now want to split the dataframe
How to solve “ValueError: y should be a 1d array, got an array of shape (73584, 15) instead.”
I am new to python and machine learning. I want to fit SVM to the training sets. Then I got an error: ValueError: y should be a 1d array, got an array of shape (73584, 15) instead. Output: So how should I fix this problem? Would appreciate a lot if any advices. Shape of y: Examples of label y are: