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What our customers say about pdfFiller
See for yourself by reading reviews on the most popular resources:
Just starting to use this - think it will work for me. Sometimes find it awkward to intuit what to do, so would really benefit from a webinar if offered free.
2017-05-25
No encuentro como ver siempre la página en Español, en ingles se me dificulta mucho aún y no puedo hacer uso de todas las herramientas
2020-03-27
What do you like best?
On line storage and file forms for easy access, easy to operate
What do you dislike?
Wish I could add signature to documents...never given direction for how.
Recommendations to others considering the product:
fairly user friendly...they do not verify before charging annual subscription
What problems are you solving with the product? What benefits have you realized?
Completing misc forms for all uses
On line storage and file forms for easy access, easy to operate
What do you dislike?
Wish I could add signature to documents...never given direction for how.
Recommendations to others considering the product:
fairly user friendly...they do not verify before charging annual subscription
What problems are you solving with the product? What benefits have you realized?
Completing misc forms for all uses
2018-01-02
Excellent customer service
Excellent customer service. I was extremely impressed with their quick response that got me exactly what I needed. Amazing! Thank you so much!
2020-03-24
Very great tool for any business…
Very great tool for any business regardless of size. It makes things so easier to be able to complete transactions seamlessly.
2019-05-08
Easy to use.
It's good that we have this product cause we can just sign PDF's without printing.
Sometimes it's hard to place the 'typing icon' in the right place for filling in information or data.
2019-07-17
Excellent and Easy to Use pdfFiller for Digital Document Management
Overall, pdfFiller is a great tool for managing digital documents. It is easy to use and allows me to quickly and accurately fill out documents. However, I think the cost of the service is a bit too expensive.
pdfFiller is an incredibly useful tool for managing digital documents. It is incredibly easy to use and allows me to quickly and easily fill out documents with accurate information. It also securely stores my documents so that I can access them anytime.
While pdfFiller is a great tool, I find the cost of the service to be a bit too expensive for what it offers. I think there should be more features included with the subscription.
2023-01-10
So far I have done only the basic I feel with this program. I am sure there are far more things I could be doing with this but at this time I am still learning
2022-02-10
Needed for real estate transactions
Needed for real estate transactions. Worked great. Was able to update pdf documents multiple times. Easy to use. Easy to download completed documents. I'll definitely use pdfFiller in the future
2021-09-08
For pdfFiller’s FAQs
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What is an example of a multi-output classifier?
An example of a multi-output classification model is a model that predicts the type and color of fruit simultaneously. The type of fruit can be, orange, mango and pineapple. The color can be, red, green, yellow, and orange. The multi-output classification solves this problem and gives two prediction results.
What is multilabel decision tree classification?
Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification.
What is multi-label classification for NLP?
Multi-label NLP refers to the task of assigning multiple labels to a given text input, rather than just one label. In traditional NLP tasks, such as text classification or sentiment analysis, each input is typically assigned a single label based on its content.
How does multi-label classification work?
Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.”
What is an example of a multi-label classification?
In multi-label classification, we have several labels that are the outputs for a given prediction. When making predictions, a given input may belong to more than one label. For example, when predicting a given movie category, it may belong to horror, romance, adventure, action, or all simultaneously.
What is an example of multilabel classification in machine learning?
Multilabel classification is a type of supervised machine learning algorithm that can be used to assign zero or more labels to each data sample. For example, a multilabel classifier could be used to classify an image to consist of both the animal such as a dog and a cat.
What is the difference between labeling and classification?
Formally, 'Classification' is a type of problem whereas labeling is a function from an object to a set of labels (maybe infinite). Much the same way Regression is a type of problem where you, again, assign a label to an object only this time the label is some real number.
What is a multi-label classification?
Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.”
What is a classification label?
With classification labels, you can classify files based on their sensitivity and enforce access policies associated with that sensitivity level. Classification labels help you identify sensitive information and encourage smarter behavior when people handle that content.
What is multilabel classification in R?
Multi-label classification (MLC) is a classification task where an instance can be simultaneously classified in more than one of the existing classes. Labeled data extracted from several domains, like text, web pages, multimedia (audio, image, videos), and biology are intrinsically multi-labeled.
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