@SVMClass def evaluate(self, X,y): outputs, _ = self.predict(X) accuracy = np.sum(outputs == y) / len(y) return round(accuracy, 2) 最后测试我们的完整代码: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from sklearn.datasets import make_classification import numpy as np # Load ...
from sklearn.svm import SVC clf = SVC(C=1, kernel='poly', degree=2) clf.fit(train_data,train_labels) print(clf.score(test_data,test_labels)) # 0.9806 test_predictions = clf.predict(test_data) cm = metrics.confusion_matrix(test_labels,test_predictions) df_cm = pd.DataFrame(cm, rang...
from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer from sklearn import decomposition, ensemble import pandas, xgboost, numpy...
Support Vector Machines (SVM): Discover how SVMs find the optimal hyperplane to separate different classes. K-Nearest Neighbors (KNN): Explore how KNN classifies data based on the closest training examples. Clustering Algorithms: Experiment with techniques like K-Means and DBSCAN for grouping similar...
from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer from sklearn import decomposition, ensemble import pandas, xgboost, numpy, textblob,stringfrom keras.preprocessing import text, sequence ...
SVC(), xtrain_tfidf_ngram, train_y, xvalid_tfidf_ngram) print "SVM, N-Gram Vectors: ", accuracy #输出结果 SVM, N-Gram Vectors: 0.5296 3.4 Bagging Model 实现一个随机森林模型:随机森林是一种集成模型,更准确地说是Bagging model。它是基于树模型家族的一部分。如果想了解更多关于随机森林,请...
的库fromsklearnimportmodel_selection, preprocessing, linear_model, naive_bayes, metrics, svmfromsklearn.feature_extraction.textimportTfidfVectorizer, CountVectorizerfromsklearnimportdecomposition, ensembleimportpandas, xgboost, numpy, textblob, stringfromkeras.preprocessingimporttext, sequencefromkerasimportlayers...
Naive Bayes in Python - ML From Scratch 05 Perceptron in Python - ML From Scratch 06 SVM (Support Vector Machine) in Python - ML From Scratch 07 Decision Tree in Python Part 1/2 - ML From Scratch 08 Decision Tree in Python Part 2/2 - ML From Scratch 09 ...
svm属于linear classifier。linear classifier: ,其中的W叫做weights,b叫做bias vector或者叫parameters interchangeably。 linear classifier可以理解为将一系列的data映射到classes上。以图像分类为例,图像的像素个数理解为维数,那么每个图片在就是在这个高维空间里的一个点。但高维是不能可视化的,为了理解,用二维草图做一...
这节讲解两个基础的损失函数的实现: from __future__ import division import numpy as np from mlfromscratch.utils import accuracy_score from mlfromscratch.deep_learning.activation_functions import Sigmoid class Loss(object): def loss(self, y_true, y_pred): ...