I have a dictionary of 100+ dataframes all have the same shape [9999 rows x 4 columns] with the following columns: ['time', 'response', 'arrival_1','arrival_2']
import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split,cross_val_score from sklearn.tree import DecisionTreeCl
I'm trying to solve the cannibals and missionaries problem in python (with some additional criteria, but the main idea is the classic one). So, in the class Gra
I'm trying to convert a predicted RasterFrameLayer in RasterFrames into a GeoTiff file after training a machine learning model. When using the demo data Elkton-
I am fairly new to coding and getting confused between average accuracy and overall accuracy. I have created a function to calculate accuracy, i then divide thi
I want to perform a random search, in classification problem, where the scoring method will be chosen as AUC instead of accuracy score. Have a look at my code f
I need you guys help to find a non linear decision boundary. I have 2 features with numerical data, I made a simple linear decision boundary (see picture below)
Problem statement: I have 150k points in a 3D space with their coordinates stored in a matrix with dimension [150k, 3] in mm. I want to find all the neighbors o
As an example, suppose there is a random forest and a logistic regression model that accept the same input data, and I want the inference result to be the avera
I'm following the book Hands-on Machichine Learning by Aurelien Geron, more specifically, where it begins to go into classifiers. I'm following the code from th
How can I use from scipy.stats import multivariate_normal to generate data? In specific, I want to create a GMM data that contains 3 columns (features) and a la
How to compute similarity(percentage) between two matrix/arrays. or find the closest array/matrix to a given array, on the basis of how similar their data value
I have defined few parameters in my config.yaml like as below. params: epochs: 10 batch_size: 128 num_classes: 10 loss_function: sparse_categorical_cros
I want to calculate shap values from a sklearn pipeline with a preprocessor and a model. When i do it with the code below I get 0 for all shape_values def creat
In scikit-learn, the GaussianMixture object has the method bic(X) that implements the Bayesian Information Criterion to choose the number of components that bet
I'm getting a keyerror 'initialized_diffuse' while calling the following API, probably after joblib.load(). import joblib .......... @routes.route("/forecast",
I built a random forest by RandomForestClassifier and plot the decision trees. What does the parameter "value" (pointed by red arrows) mean? And why the sum of
I want to customise the GRU-RNN cell from tensorflow. But i dont know which function i need to change from standart GRU from tensorflow. i want to modify GRU ce
I want to build a new computer for Data Science purposes. What do you think about this hardware: https://www.ldlc.com/configurateur-pc/23fe088422141bb69274a13ca
Is the GlobalAveragePooling1D Layer the same like calculating the mean with a custom Lambda Layer? The data is temporal, so x has shape (batch, time, features)