Quem achar que ele ajudou em alguma coisa, deixa um joinha ali em cima. Total running time of the script: ( 0 minutes 0. Este vídeo é um tutorial em português (gravado em 2014) do programa SciDavis. ![]() plot ( diabetes_X_test, diabetes_y_pred, color = "blue", linewidth = 3 ) plt. scatter ( diabetes_X_test, diabetes_y_test, color = "black" ) plt. coef_ ) # The mean squared error print ( "Mean squared error: %.2f " % mean_squared_error ( diabetes_y_test, diabetes_y_pred )) # The coefficient of determination: 1 is perfect prediction print ( "Coefficient of determination: %.2f " % r2_score ( diabetes_y_test, diabetes_y_pred )) # Plot outputs plt. predict ( diabetes_X_test ) # The coefficients print ( "Coefficients: \n ", regr. Creates a new note window in the project. SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. What does it look like Don't hesitate to contact us if you think you can help us make SciDAVis even better for everyone. Choose Graph -> Add/Remove curve and add the data you want to plot. SciDAVis is a free application for Scientific Data Analysis and Visualization. Set the opacity of background and canvas color to 0. Invoke the plot details dialog using Format -> Plot. Azodicarboxylate Overload Journal The Scidavis Handbook Home, SciDAVis Data Plotting and Curve Fitting with SciDAVis David P. ![]() fit ( diabetes_X_train, diabetes_y_train ) # Make predictions using the testing set diabetes_y_pred = regr. The project is the main container of SciDAVis, it can include tables, plots and notes. Tell SciDAVis not to guess the position, that is, put it in the top-left corner. LinearRegression () # Train the model using the training sets regr. load_diabetes ( return_X_y = True ) # Use only one feature diabetes_X = diabetes_X # Split the data into training/testing sets diabetes_X_train = diabetes_X diabetes_X_test = diabetes_X # Split the targets into training/testing sets diabetes_y_train = diabetes_y diabetes_y_test = diabetes_y # Create linear regression object regr = linear_model. # Code source: Jaques Grobler # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn import datasets, linear_model from trics import mean_squared_error, r2_score # Load the diabetes dataset diabetes_X, diabetes_y = datasets.
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