https://elitedatascience.com/machine-learning-algorithms

https://towardsdatascience.com/comparative-study-on-classic-machine-learning-algorithms-24f9ff6ab222

Article Advantages and Disadvantages of Using Artificial Neural Netw...

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8153&rep=rep1&type=pdf

https://ieeexplore.ieee.org/abstract/document/5997308/

https://www.pnas.org/content/88/24/11426.short

Article Comparing regression, naive Bayes, and random forest methods...

You can have SVR:

Support Vector Regression (SVR) using linear and non-linear kernels

Toy example of 1D regression using linear, polynomial and RBF kernels.

📷

print(__doc__) import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt # ############################################################################# # Generate sample data X = np.sort(5 * np.random.rand(40, 1), axis=0) y = np.sin(X).ravel() # ############################################################################# # Add noise to targets y[::5] += 3 * (0.5 - np.random.rand(8)) # ############################################################################# # Fit regression model svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1) svr_lin = SVR(kernel='linear', C=100, gamma='auto') svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1, coef0=1) # ############################################################################# # Look at the results lw = 2 svrs = [svr_rbf, svr_lin, svr_poly] kernel_label = ['RBF', 'Linear', 'Polynomial'] model_color = ['m', 'c', 'g'] fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True) for ix, svr in enumerate(svrs): axes[ix].plot(X, svr.fit(X, y).predict(X), color=model_color[ix], lw=lw, label='{} model'.format(kernel_label[ix])) axes[ix].scatter(X[svr.support_], y[svr.support_], facecolor="none", edgecolor=model_color[ix], s=50, label='{} support vectors'.format(kernel_label[ix])) axes[ix].scatter(X[np.setdiff1d(np.arange(len(X)), svr.support_)], y[np.setdiff1d(np.arange(len(X)), svr.support_)], facecolor="none", edgecolor="k", s=50, label='other training data') axes[ix].legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=1, fancybox=True, shadow=True) fig.text(0.5, 0.04, 'data', ha='center', va='center') fig.text(0.06, 0.5, 'target', ha='center', va='center', rotation='vertical') fig.suptitle("Support Vector Regression", fontsize=14) plt.show()