SVM works by finding a hyperplane in an N-dimensional space (N number of features) which fits to the multidimensional data while considering a margin.
Image processing is a way to convert an image to a digital aspect and perform certain functions on it, in order to get an enhanced image or extract other useful information from it. It is a type of signal time when the input is an image, such as a video frame or image and output ca...
19 plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap) /opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py in predict(self, X) 307 Predicted class label per sample. 308 """ --> 309 scores = self.decision_function(X)...
# Create a Confusion Matrixplt.figure(figsize=(8,8))sns.heatmap(cm,annot=True,fmt='d',cmap='Greens')plt.title('Confusion Matrix')plt.ylabel('True label')plt.xlabel('Predicted label')plt.show() This is the output: Random Forest Confusion Matrix Output ...
import seaborn as snsimport matplotlib.pyplot as pltcorrmat = data[columns].corr()mask= np.zeros_like(corrmat)mask[np.triu_indices_from(mask)] = Truesns.heatmap(corrmat,vmax=1, vmin=-1,annot=True, annot_kws={'fontsize':7},mask=mask,cmap=sns.diverging_palette(20,220,as_cmap=True)...
SciPy provides the fftpack module, which is used to calculate Fourier transformation. In the example below, we will plot a simple periodic function of sin and see how the scipy.fft function will transform it. from matplotlib import pyplot as plt ...
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alpha = 0.75, cmap = ListedColormap(('red', 'green'))) plt.xlim(X1.min(), X1.max()) plt.ylim(X2.min(), X2.max()) for i, j in enumerate(np.unique(Y_Set)): plt.scatter(X_Set[Y_Set == j, 0], X_Set[Y_Set == j, 1], c = ListedColormap(('red', 'gr...
Then use matplotlib to plot 30 random images from the dataset with their labels above them. This step requires a load_data function that's included in an utils.py file. This file is included in the sample folder. Make sure it's placed in the same folder as this notebook. The load_...
("Hierarchical Clustering Dendrogram") dendrogram(Z) plt.show() # K-means Clustering kmeans = KMeans(n_clusters=3, random_state=0).fit(X) labels = kmeans.labels_ plt.figure(figsize=(10, 7)) plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='prism') plt.title("K-means ...