sklearn-pandas

问题 I am trying to import sklearn in my jupyter notebook on a macOS Mojave 10.14.3. macOS comes standard with python 2.7. I have installed python3 in addition python 2.7. I am launching jupyter from a virtual env with python3. When I launch jupyter and open a notebook it shows it is running python 3.7.x. When I list "!pip show scipy" from inside the notebook it says scipy 1.2.1 is installed. Still, with all the above, when I say "import scikit" or "import sklearn" I get a ModuleNotFoundError: No

问题 I made a program using scikit-learn, it was working fine for months. Yesterday, when I run it again it showed me the error ImportError: DLL load failed: The specified module could not be found. I searched for an answer on StackOverflow and other websites. I also checked for the requirements. I am using PyCharm Community edition 2019.2.1, 64 bit. my libraries status:- joblib==0.14.0 numpy==1.17.2 pandas==0.25.1 scikit-learn==0.21.3 scipy==1.3.1 python==3.7.4, 32 bit Error in the line:- import

问题 I'm trying to learn how to implement MICE in imputing missing values for my datasets. I've heard about fancyimpute's MICE, but I also read that sklearn's IterativeImputer class can accomplish similar results. From sklearn's docs: Our implementation of IterativeImputer was inspired by the R MICE package (Multivariate Imputation by Chained Equations) [1], but differs from it by returning a single imputation instead of multiple imputations. However, IterativeImputer can also be used for multiple

问题 When trying to install Python's sklearn package on Windows 10 using pip I am given an EnvironmentError that tells me there is no such file or directory of a specific file: ERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: 'C:\Users\Rik\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\sklearn\datasets\tests\data\openml\292\api-v1-json-data-list-data_name-australian-limit-2

问题 When trying to install Python's sklearn package on Windows 10 using pip I am given an EnvironmentError that tells me there is no such file or directory of a specific file: ERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: 'C:\Users\Rik\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\sklearn\datasets\tests\data\openml\292\api-v1-json-data-list-data_name-australian-limit-2

问题 I have a training data CSV with three columns (two for data and a third for targets) and I successfully predicted the target column for my test CSV. The problem is I need to inverse transform the results back to strings for further analysis. Below is the code and error. from sklearn import datasets from sklearn import svm from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.cross_validation import train_test_split from sklearn

问题 I'm trying to fit a KNN model on a dataframe, using Python 3.5/Pandas/Sklearn.neighbors. I've imported the data, split it into training and testing data and labels, but when I try to predict using it, I get the following error. I'm quite new to Pandas so any help would be appreciated, thanks! import pandas as pd from sklearn import cross_validation import numpy as np from sklearn.neighbors import KNeighborsRegressor seeds = pd.read_csv('seeds.tsv',sep='\t',names=['Area','Perimeter',

问题 I installed Python package shap for plotting. conda install -c conda-forge shap After installing, I import shap in jupyter notebook but got error. import shap --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-132-efbb001a1501> in <module> ----> 1 import shap ~\AppData\Local\Continuum\anaconda3\lib\site-packages\shap\__init__.py in <module> 3 __version__ = '0.29.3' 4 ----> 5 from .explainers.kernel import

问题 I have this data set with 78 columns and 5707 rows. Almost every column has missing values and I would like to impute them with IterativeImputer. If I understood it correctly, it will make a "smarter" imputation on each column based on the information from other columns. However, when imputing, I do not want the imputed values to be less than the observed minimum or more than the observed maximum. I realize there are max_value and min_value parameters, but I do not want to impose a "global"

问题 I have this data set with 78 columns and 5707 rows. Almost every column has missing values and I would like to impute them with IterativeImputer. If I understood it correctly, it will make a "smarter" imputation on each column based on the information from other columns. However, when imputing, I do not want the imputed values to be less than the observed minimum or more than the observed maximum. I realize there are max_value and min_value parameters, but I do not want to impose a "global"