I followed the steps given by the site (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html) and install the following: And the terminal return that “Successfully installed h2o-3.36.1.1”. However, when I run the code to optionally initialize H2O in Python and run a demo to see H2O at work, using this script: it returns this error: Could someone tell me why I can’t run h2o? Thank you
Tag: machine-learning
Get number of Clusters (3D)
I have a question about clustering. When you’re using k-nearest neighbour algorithm, you have to say, how many clusters you’re expecting. My problem is now, that I have some runs, where the number of clusters varies. I checked, and there are some methods how you can restrict, how many clusters you have, but these algorithms work for a two-dimensional problem.
Pipline with SMOTE and Imputer Errors
i am trying to create a pipeline that first impute missing data , do oversampling with the SMOTE and the the model my code worked perfectly before i try smote not i cant find any solution here is the code without smote And here’s the code after adding smote Note: I tired importing make pipeline from imlearn when i import
Image processing in Tensor flow TFX pipelines
I am trying to get a Tensorflow TFX pipeline up and running using the MNIST dataset. Setup pipeline paths Write the data to TF.record format and save in eval and train dirs. NOTE that the MNIST data starts as a numpy array 28×28 and is converted to a bytestring to enable it to be encoded as part of the Tf.record.
Reading in file names from a tensor in Tensorflow
Context: I am trying to make a GAN to generate images from a large dataset, and have been running into OOM issues when loading in the training data. In an effort to solve this, I am trying to pass in a list of file directories and read them in as images only when needed. Issue: I do not know how
Linear regression prediction based on group of data in test set
I have a simple dataset which looks like this: I created a simple LR model to train and predict the target variable “sales”. And I used MAE to evaluate the model My code works well, but what I want to do is to predict the sales in the X_test grouped by hour of the day. In the above dataset example,
How to get SHAP values for each class on a multiclass classification problem in python
I have the following dataframe: For which I want to run a classification algorithm in order to predict the 3 classes So I split my dataset into train and test and I run an xgboost Now I would like to get the mean SHAP values for each class, instead of the mean from the absolute SHAP values generated from this
Job type(Full Time , Part Time) detection with Machine learning model in Python
I have a dataset of jobs where I have columns “Title” ,”Description” , “City” etc. and “Best Jobs” column. Output of the dataset is “Best Jobs” where I have two outputs(Yes , No) Yes mean jobs are part time and No , mean job is full time. I want to train any Machine learning model. Firstly I want to train
name ‘Bidirectional’ is not defined
Im following this tutorial and right when I want to initialize a sequential keras, like the code below: I get an error saying : What is the problem ? it is the exact same code as in the tutorial. Answer You’re most likely missing the import statement from the tensorflow package. It appears that’s there is a link to the
Chronologically Propagating Data into a Keras LSTM
I had a question about using LSTMs for processing data over time. That is, how can I feed data one-by-one into an LSTM, without the LSTM forgetting about my previous inputs? I had looked through the Keras “stateful” argument a bit, but it had only made me more confused. I’m not sure whether it’s relevant or not for my purposes.