how to clean data using Python (string part) 1. substring str.replace() : find and replace 输出: blue is my favorite color 2. str.title():returns a copy of the string with the first letter of each word transformed to uppercase 3. 用函数去掉字符串中不需要的符号 s... ...
解决ValueError: Expected 2D array, got 1D array instead: Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample 在使用机器学习算法进行数据建模时,经常会遇到输入数据的维度问题。其中一个常见的错误是"ValueE...
y_train) print("SVM on B is done...") # X_test[best_feature]= np.array(X_test[best_feature]).reshape(1, -1); print X_test[best_feature]; predictA=clfA.predict(X_test[best_feature]); predictB=clfB.predict(X_test[best_feature]); for feature...
Kaggle Exercise: Explore Your Data 目前在kaggle学习,读取处理文件一般用的是pandas,记录一下卡住我很久的点。 我了解到的有2种方式: 第一种是:home_data['LotArea'].mean() 第二种是:home_data.YearBuilt.max() 注意:LotArea、YearBuilt均为home_data的属性;而mean、max方法为取均值、最大值 记录一下...
Python的Reshape your data either using array.reshape(-1, 1) if your data has a single feature or 在使用sklearn进行数据处理的时候出现了报错,如题所示; 报错地方的代码如下 #dataitem = x_train[1]y_predicted.append(clf.predict(dataitem))
array=[4742.923398.2491.92149.2070. ]. Reshape your data either using array.reshape(-1,1)if your data has a single featureor array.reshape(1,-1) if it contains a single sample. 这是在git上面看到的一个国际友人的解答。 原文,如下:
解决ValueError: Expected 2D array, got 1D array instead: Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample 在使用机器学习算法进行数据建模时,经常会遇到输入数据的维度问题。其中一个常见的错误是"ValueE...
成功解决ValueError: Expected 2D array, got 1D array instead: Reshape your data either using array.reshap 目录 解决问题 解决思路 解决方法 解决问题 ValueError: Expected 2D array, got 1D array instead: Reshape your data either using array.reshape(-1, 1) if your data has a single feature or ar...
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample. 定位到我的错误代码: input_features=preprocessing.StandardScaler().fit_transform(features) 改正方法: input_features=preprocessing.StandardScaler().fit_...
train[“label”]=train[“label”].fillna(“1”); test[“label”]=test[“label”].fillna(“1”); train[“num_failed_logins”]=train[“num_failed_logins”].fillna(“0”); test[“num_failed_logins”]=test[“num_failed_logins”].fillna(“0”); train[“num_access_files”]=train[“...