Tag: pca

Make available .best_params_ after pipeline

How to go about making available the clf.best_params_ after carrying a pipeline? For the code I have below, I get an: AttributeError: ‘GridSearchCV’ object has no attribute ‘best_params_’ Here is my code: Answer Your clf is never fitted. You probably meant clf.fit(X_train,y_train). Also, np.linspace(10,50,11) yields floats, while max_depth expects ints, so this may fail and you should probably add a

How to put importance coefficients to features before kmeans?

k-means machine-learning pca python scikit-learn

Lets say I have the given dataframe And I would like to find clusters in these rows. To do so, I want to use Kmeans. However, I would like to find clusters by giving more importance to [feature_1, feature_2] than to the other features in the dataframe. Lets say an importance coefficient of 0.5 for [feature_1, feature_2] , and 0.5

shape error while concating columns after Principal Analysis in csv

data-science pandas pca python python-3.x

I am applying PCA in my csv data. After normalization, seems PCA is working. I want to plot projection by making 4 components. but I am stuck with this error : This is my code: I guess I am getting error while concat my components and df[‘type’]. Can I get idea to get rid of this error? Thank you. Answer

TypeError: SparseDataFrame() takes no arguments in Principal Component Analysis using Light_FAMD

pca python

When attempting to perform a FAMD according to the instructions on https://pypi.org/project/light-famd/#factor-analysis-of-mixed-data-famd, I keep getting the same error over and over again, namely: TypeError: SparseDataFrame() takes no arguments. How to fix this problem? It occurs not only on my own data set but also whenever I try it on a basic randomly-generated data set such as created like this: The

Sklearn PCA explained variance and explained variance ratio difference

covariance pca python scikit-learn

I’m trying to get the variances from the eigen vectors. What is the difference between explained_variance_ratio_ and explained_variance_ in PCA? Answer The percentage of the explained variance is: The variance i.e. the eigenvalues of the covariance matrix is: Formula: explained_variance_ratio_ = explained_variance_ / np.sum(explained_variance_) Example: Also based on the above formula: 7.93954312 / (7.93954312+ 0.06045688) = 0.99244289 From the documentation:

Principal Component Analysis (PCA) in Python

pca python scikit-learn

I have a (26424 x 144) array and I want to perform PCA over it using Python. However, there is no particular place on the web that explains about how to achieve this task (There are some sites which just do PCA according to their own – there is no generalized way of doing so that I can find). Anybody