Whenever we have a dataset to be pre processed, before feeding it to the model we convert the categorical values to numerical values for which we generally use LabelEncoding, One Hot encoding etc techniques but all these are done manually going through each column. But what if are dataset is huge in terms of columns(eg : 2000 columns), here it wont be possible to go through each column manually, in such cases how do we handle encoding? Are there any …
My data set contains multiple columns with first name, last name, etc. I want to use a classifier model such as Isolation Forest later. Some word embedding techniques were used for longer text sequences preferably, not for single-word strings as in this case. So I think these techniques wouldn't be the way that will work correctly. Additionally Label encoding or Label binarization may not be suitable ways to work with names, beacause of many different values on the on side …
I've imported WordPress post data onto a new site and noticed I have strange characters showing on pages and Blog posts. It usually shows where apostrophes should be. I've searched multiple solutions to UTF8 & latin1 solutions with success. I've looked at my database and the characters are showing there too.
I have a standard rest API setup in WP. The results are displayed in an IOS App. Now the problem occurres, that single and double quotes and & are returned in the JSON as Unicode Decimal Code: eg. &#8216. All other characters seem fine. Any Ideas to that?
I am tasked with using 1-of-c encoding for a NN problem but I cannot find an explanation of what it is. Everything I have read sounds like it is the same as one hot encoding... Thanks
I'm using wp_insert_post I loop over a text file one row at a time and for each row I create a post. The text is set as the `post_title', for text that is not utf8 the post inserts but with an empty title. Why does that happen, if I'm able to create a post in the backend admin using non utf8 chars it looks like WordPress converts the encoding in the backend. How can I bypass this with wp_insert_post and …
I am trying find commonly used techniques when dealing with high cardinality multi-valued categorical variables. I am currently using a dataset with a feature CATEGORY which has a cardinality of ~20,000. One-hot encoding does not make sense has it would increase the feature space by too much. Each observation in my dataset can take multiple values for the CATEGORY feature, for instance, row 1 could have the value a but row 2 could have the values a, b, c, d …
I have a simple time-series dataset. it has a date-time feature column. user,amount,date,job chris, 9500, 05/19/2022, clean chris, 14600, 05/12/2021, clean chris, 67900, 03/27/2021, cooking chris, 495900, 04/25/2021, fixing Using Pandas, I split this column into multiple features like year, month, day. ## Convert Date Coloumn into Date Time type data["date"] = pd.to_datetime(data["date"], errors="coerce") ## Order by User and Date data = data.sort_values(by=["user", "date"]) ## Split Date into Year, Month, Day data["year"] = data["date"].dt.year data["month"] = data["date"].dt.month data["day"] = data["date"].dt.day …
I am trying to build a pipeline in order to perform GridSearchCV to find the best parameters. I already split the data into train and validation and have the following code: column_transformer = make_pipeline( (OneHotEncoder(categories = cols)), (OrdinalEncoder(categories = X["grade"])), "passthrough") imputer = SimpleImputer(strategy='median') scaler = StandardScaler() model = SGDClassifier(loss='log',random_state=42,n_jobs=-1,warm_start=True) pipeline_sgdlogreg = make_pipeline(imputer, column_transformer, scaler, model) When I perform GridSearchCV I am getting the follwing error: "cannot use median strategy with non-numeric data (...)" I do not understand why am …
I moved over a development site to the client's hosting server using the WP Clone plugin. It seemed to work just fine, until I noticed a bunch of odd question marks where things like em-dashes and apostrophes should be. It appears to be a unicode issue, but the only difference I can tell between the two servers is that the client-side is using utf8mb4_unicode_c and my development server is using utf8_unicode_ci. If I copy and paste a page from the …
I am looking at some examples in kaggle and I'm not sure what is the correct approach. If I split the training data for training and validation and only encode the categorical data in the training part sometimes there are some unique values that are left behind and I'm not sure if that is correct.
so my question is similar to the one found here, following the example there i am trying to force Tiny MCE to encode backslashes...... currently all i am doing to test this is setting a break point on the page for the following line tinymce.init( init ); then i run the following in the console init.entities += ",92,#92"; init.entity_encoding = "named"; I see the values update in the init object but my \ is not converted.... not really sure what …
I am working on a website where I am using a custom template. My site having German character. When I use the visual editor(without template) it displays perfectly. Link But when I am using a custom template for static content it won't show German characters. Link
I was going through the official documentation of scikit-learn learn after going through a book on ML and came across the following thing: In the Documentation it is given about sklearn.preprocessing.OrdinalEncoder() whereas in the book it was given about sklearn.preprocessing.LabelEncoder(), when I checked their functionality it looked same to me. Can Someone please tell me the difference between the two please?
Is there a way (other than manually creating dictionaries) to one hot encode sequences in which not all values can be present in a sequence? sklearn's OneHotEncoder and numpy's to_categorical only account for the values in the current sample so for example, encoding DNA sequences of 'AT' and 'CG' would both be [[1, 0], [0, 1]]. However, I want A, T, C, and G to be accounted for in all sequences so 'AT' should be [[1, 0, 0, 0], [0, …
I was trying to encode the string values of the feature 'ProductCategory' into integer values but I got this error. Kindly help. And I would also like to ask if label-encoding this feature would not force my model to misrepresent the integer values as 0<1<2. Thanks.
I just learned that regularizing the mean encoding reduce the leakage hence generalize better than mean encoding without it but I made 2 submissions with XGB in predict future sales competition on Kaggle with the naive mean encoding method and got RMSE = 1.152 and with 5 folds validation and got RMSE = 1.154 which was a surprise for my. Can any one explain why this may happen ? also after making the kfolds regularization every item_id has multiple mean …
My website is a daily deals and offers site. I promote many online stores with affiliate links. I have created a php script to detect any merchant's link (ex- Amazon) and convert it to my affiliate link. Example - (Script Name: redirect.php) If you go to - https://example.com/redirect.php?link=https%3A%2F%2Fwww.amazon.com%2F It will land you to Amazon site with my affiliate id attached to the url. My Requirement:- I have a separate custom field called "rehub_offer_product_url" where I put the normal link to …