Weka Reviews

My main use case for Weka is data exploration and data processing and data mining.

I can give a quick, specific example of how I've used Weka for data exploration or data processing: we have a subject called data exploration and data mining where we extract data from companies and process it. We take the data from companies and filter out the correct data that we want to work on, and then we use J48 and KNNs, decision trees, and all to find the exact data processors, cleanse the raw data by removing null values and applying these regressions to get our final data.

I also have something else to add about how I use Weka: we use it for clustering and association as well, grouping the data points we are using. We used bank data last time, which had many null values and many true and false values. We needed to predict the S value and the no value for the term insurance. We used clustering and association and visualized the data with graphs to show the professor, so it's a good thing.

The best features Weka offers in my opinion are simplicity and the ability to extract very easily. The visualization and graphs tell us the depth in it. There are many other features in data processing as well and classifications where regressions are better compared to other apps, so I think this is a very good app.

The visualization features stand out to me because, as I told you, we have used bank data that people can't understand just by seeing.

Using Weka has positively impacted my studies and projects because it has saved my time firstly. Weka processes a large set of data sets without needing to write any type of code. It can handle high-performance data, and we worked on bank data, which is enterprise data, so Weka performs well on enterprise data. We can compare different algorithms like J48, KNNs, and regressions.

I can share specific outcomes or metrics, as it obviously saved a lot of time. We can directly remove or add attributes without coding, and we can clearly see by graphs which attribute is perfect for data analysis or data performance. Efficiency also improved in our results, and since we don't need to code, speed and productivity improved as well. We also noticed that costs are less compared to using other apps.

Weka can be improved in some areas. As a one-year user, I can share my experiences. I have tried installing other packages into Weka using the inbuilt package manager or adding modern algorithms that I wish to apply to my data sets or data processing. I think Weka needs to improve in that and also in integrating Python into Weka, which would help users much more.

I chose eight out of ten because we need improvements in Weka, such as installing inbuilt packages. Weka uses skips such as N+2 or N+4 and eight-node, which makes sense basically, but I think it should improve in the package attribute, such as configuring Python easily and adding modern algorithms into Weka more easily.

I have been using Weka for the past one year.

Weka is stable for me; I have not experienced any issues or crashes.

Weka's scalability for my projects depends on the data sets used. As students, if we use the largest data sets, I think Weka struggles with large data sets imported into it. Sometimes it struggles when we work with larger data sets.

I have not reached out for customer support for Weka, but my friend has, and he told me they were very good and available twenty-four hours a day, seven days a week.

I did not use any different solution for data mining or processing before; I started with Weka and love it, using it every day.

I have seen a return on investment from using Weka.

The relevant metrics for return on investment, as we are using a free version, obviously mean saved time, productivity, and scalability for the company. Those are the three main reasons for us.

My experience with pricing, setup cost, and licensing for Weka is that I think it's a fair price since we are using it academically, so it is completely free to download and use. The license is also free, allowing us to modify and share software freely among friends or classmates.

Before choosing Weka, I evaluated no other options; we directly came to Weka because it is popular for the kinds of work we do and is free, needing no licenses or purchases, making it good for educational and research purposes.

My advice for others looking into using Weka is that it is excellent for data processing if it meets your requirements, especially if you are an aggressive data processor or want to use the modern algorithms in Weka without coding, even with small data; Weka is so good to use in data processing, data aggression, data regression, and data combining, clustering, and all.

I have no additional thoughts about Weka before we wrap up. I have given this review a rating of eight out of ten.

I use Weka in my process, study, and sciences for research with my students.

I have experience with Weka's Experiment feature, as I use all the tools available in Weka.

I utilize Weka's Experiment feature for my research.

I have experience with Weka's collection of data pre-processing tools. I use many, though not all, of the tools for data preparation, and they are very useful for preparing data for the next analysis.

I use support for the Java language in Weka, but I do not use other language support.

In my opinion, Weka is very useful. I think perhaps a new function with a new algorithm in data mining could be developed. The algorithms currently available in Weka are very good, and all functions are very useful.

I have found that I need to use Weka with R and different databases, which has been very useful for me.

I am satisfied with Weka's visualization tool.

Weka is very easy to use, is very complete, and provides many benefits to the end user. It has many algorithms in data mining and many algorithms to prepare data for the next process in data mining. Weka has many graphical interfaces, though it may not have as many tools for analyzing results.

I think Weka could improve by providing more analysis options, though I am uncertain about specific recommendations at this moment.

I have been working with Weka for about six years.

I think Weka is very stable, and I give it a 10 mark.

Scalability for Weka refers to the ability to expand, the ability to increase the number of users, and the ability to increase the amount of data, among other factors. I rate the overall scalability of Weka as a 10.

Technical support for Weka is very good, and I rate it a 10.

I do not compare Weka to any other product because Weka is first place for me.

Weka's initial setup is very simple.

I installed Weka without help, and my student used it without help as well. I prepared one book about Weka for my student.

I use the free version of Weka, and I do not know the cost of the professional version or the lite version.

I can recommend Weka to other users.

I give this review a rating of 10.

In my university, we used Weka. Weka was used in marketing by my professor, I was preparing a presentation for PhD proposals specifically on energy consumption and renewable resource utilization. So, I needed data mining tools.

Weka's is fine for basic results, no big issues there. 

But if you need visualizations, it's not the best choice. For someone who just needs analysis, Weka serves the purpose. 

But nowadays, good visuals are crucial for presenting findings and telling a story. In my practical experience at the university, when people need quick results with strong visualizations, I don't recommend Weka.

I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results. 

Moreover, a new user interface would be great, especially for beginners. Something that guides them through the available tools and helps them achieve their goals. I haven't seen anything like that myself, though maybe it's there and I missed it. 

I worked with Weka for about a year and a half.

It is a stable product.

It is very easy to scale. I would rate the scalability a seven out of ten. 

My primary interest lies in energy analytics, optimization, and related fields.

Unfortunately, I couldn't complete the project due to work commitments, but I realized KNIME would have been far better suited for such analysis. For data mining, I had two main options: Python libraries and KNIME. KNIME is a bit expensive, requiring membership, so I opted for Python's ease of use.

Python libraries offer free access and flexibility for various data mining tasks. Python is better than some of the state-of-the-art software.

Weka's ease of use is around seven out of ten. Anyone can pick it up. But the results could be improved, especially visualizations.

We used it offline, directly on the software.

The deployment is in between. Not super fast, but I haven't used other software for comparison. 

Python libraries seem quicker at giving results, but Weka's not terribly slow for backups. It took a good three hours, but not excessively long.

Weka is free and open-source software. That is why I used it over KNIME.

I recommend it. It's been around in the industry for a while, and I consider it one of the best, except for the visualization issue. It does not have good user-friendly graphics. It does not offer good visualization or presentation. Otherwise, it's very viable.

Overall, I would rate the solution a seven out of ten.

I run the method "Association rules" in Weka, and also regression trees like RandomForest.

Weka is written in Java like other standalone programs but as I don't know Java, I don't use its "Simple CLI" application. In Weka, I use menu options. I am mainly a programmer in R and although Weka has parsers for R, I don't use them.

If I didn't know any language, Weka would be my solution. But as I know R and Python, Weka is not my first solution although I acknowledge that for methods that I don't usually handle, Weka is the quickest way to approach them.

If the user is not a programmer, Weka is the best option to approach machine learning.

Every method of machine learning is available on Weka. They may not be the ultimate solution, as R has many solutions since many university scholars use R to improve knowledge. 

In Weka, every method is at your fingertips. In a language, the method must first be installed.

I am not sure but I think that machine-learning solutions in Weka cannot be uploaded to a server for production. 

Nowadays solutions are not simple like in toy regressions that can be manipulated in worksheets or in other languages. The solution, frequently black-box ones, must be uploaded on a server from the software of the solution, and I don't think Weka does it.

I have been working on Weka for ten to twelve years.

From a stability standpoint, I rate this solution somewhere between seven and eight out of ten.

In terms of scalability, I think Weka is not prepared to handle a large number of users. However, as a freelancer working alone and not with five or six other people, this is not an issue for me.

Although I have never contacted Weka's technical support team, I know they have a system similar to Google Groups. However, I am uncertain whether Weka created these groups or if they are affiliated with The University of Waikato and not Google. 

I never asked for access to such a group.

Neutral

I believe that the setup for this solution is simple. It is not difficult. It approaches the easy usual way for non-programmers. In comparison, setting up programs in R is much more difficult as it requires identifying and downloading specific packages.

I am currently using the freeware version of Weka on my desktop. As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if there is a commercial product. However, I am aware that the team at Waikato University created MOA (Massive Online Analysis) for data stream analysis, somehow analysis on the fly as new cases get in. They can handle very large datasets with millions of rows, which is beyond the capacity of a desktop. 

I am not in data streaming so I cannot say much. 

This online solution is different from what I mentioned above on uploading a machine-learning object into production. To the best of my knowledge, MOA is research, not production.

I only compared Weka to R and Python and made no other comparisons. Additionally, I know several online solutions are available, but I do not use any of them. All my research is done on a desktop, not online.

In a new machine learning job, if the method is a bit foreign to me, if I have to do it in R, it could be a tedious task. First, I need to identify the libraries required for the new methodology. This can involve identifying two, three, or even four libraries. Then, I need to read their manuals thoroughly. This is time-consuming.

In Weka, as all machine learning tools are on my desktop, I easily find out the method.

As a freelancer, people send me datasets, and I work on the statistics at home before providing the solution. When a solution needs to be implemented on a server, server programmers install it on the server. This is similar to Power BI, where I prepare files on my desktop, and someone else uploads them to the server for others to access. I think I cannot send a Weka solution to a server programmer.

In Weka, anyone can run the program without being a programmer, which is a good feature since the entry cost is very low.

We are using Weka for machine-learning purposes.

Weka eliminates the need for coding, allowing you to easily set parameters and complete the majority of the machine learning task with just a few clicks.

The visualization of Weka is subpar and could improve. Machine learning and visualization do not work well together. For example, we want to know how we can we delete empty cells or how can we fill in the empty cells without cleaning the data system and putting it together.

In a future release, having a data-cleaning feature would be beneficial.

I have been using Weka for three years.

I rate the stability of Weka a ten out of ten.

I am the only one using this solution in my company. I am using the solution at its full capacity.

I rate the scalability of Weka a ten out of ten.

I have not used the support for Weka.

I previously used Python and R and some additional software.

The initial setup of Weka is simple. The process does not take long.

One person can deploy the solution, I did it on my own.

I have received a return on investment using Weka.

The solution is free and open-source.

I would recommend this solution to those who do not understand coding very well.

I rate Weka a nine out of ten.

We discovered that it could be used well with labs, thanks to the attributes and instances.

I like the fact that it doesn't require programming skills.

They have YouTube videos you can watch and learn and stuff. It’s pretty straightforward. I know that it's user-friendly.

It doesn’t cost anything to use the product.

There are some reviews a few people said they had used it, and they said it was good. With the little that I have done, I’ve noted that it has a good interface. There's data visualization. You can filter and create different scenarios and instances, add missing data, et cetera.

I haven't really utilized other automation learning tools. I'm just starting to use Weka. I'm not going to be able to say, "Oh, this is the area they should improve on." I'm mostly learning how to use it.

I have heard people say that they didn't like it. Since it was built by a university for machine learning purposes, they don't have good support. So I know that's an area for improvement.

A few people said it became slow after a while.

I'm currently teaching myself to use Weka. I haven't used it that long.

The solution doesn’t really have very good technical support.

I’ve used other automation tools.

The initial setup is easy. You download and install it, and then you learn how to use it.

The solution is very low-maintenance. It is easy to maintain.

The solution is open-source and free to use.

Users are often students and data scientists mostly. Most enterprises and maybe small business owners might use this product.

People should check it out. Depending on what they want to achieve with it and what they need, it might be a solution to look into.

I’d rate the solution a seven out of ten. It's free, and it's easy to use. You don't need to teach yourself, and video guides help you. There are books. There are literally online e-books on machine learning using Weka. It’s pretty straightforward.

I've handled different projects with this solution. After college, I've handled different projects. The most recent project that I handled was for a company from India. They were looking for a measure classification in regards to the type of engines that cars have, and the pollution levels that they have.

There was a mixture of text data that had to be classified. There was the need to transform the text data to a data type that would be easily classified. When employing text data you can't do classification directly. I had to clean the data and program all the variables to suit the required information.

The client has not provided me with a review yet.

However, I have benefited greatly as it's given me the confidence to tackle tedious projects that clients did not want to tackle in either Python or any other data analytics or data science software. By gaining that confidence, knowing very well that you have enough analytical skills, and now translating that to software-based platforms and juggling around with the same, has actually given me some level of proof and confidence. 

Besides experience, the fear of not wanting to handle things is what might hold you down. As far as I know, with the number of years that I've used Weka, the confidence part is the most important item I receive from the solution.

The features that I found most valuable are the classification features. They have a lot of information and a lot of intel. With classification, there's always a chance to split the data into two datasets. You can split one metadata into 92 datasets during that train or test, and the performance can easily be identified after you've trained a model. 

With clustering, if it's a yes, it's a yes, if it's a no, it's a no. It gives you a 100% level of accuracy of a model that has been trained, and that is in most cases, usually misleading. Classification is highly valuable when done as opposed to clustering.

If you were to open the software, there's a section written filter. Then you'd choose your filtering. The filter section lacks some specific transformation tools. If you want to change a variable from a numeric variable to a categorical variable, you don't have a feature that can enable you to change a variable from a numeric variable to a categorical variable. This needs to be improved. 

Also, when you go to classification, there are some cases in which, under any employed data, under the classification section that you can not actually use tests data alone or trend data alone. Under classification and clustering as well, they should give options to only supply when you're making classification or performing classification on a dataset, then there needs to be an option to either use at trend data first, and then you supply a test data later on.

If they went full open-source, like Python and R, it would help the growth of the solution.

I used Weka in my undergraduate studies. I've majored in mathematics for the past several years.

I had used the solution for three years, as I started using Weka when I was in the second year of school for daily tasks. Then I used it for two years for my masters. I didn't use it for my Ph.D. program as I was researching how to use Python and integrate it. During my studies, I used it for five years and in the field, in the actual field of employment, for contracts and employment, I've also employed it for another two years, so that makes it a total of seven years that I've used the solution.

The stability depends on the predictor variables and the independent variables that have been employed. You have to find predictor variables that are a true representation of the response variable. If the predictor variables are a true presentation of the response variables or variable, then you definitely have higher percentages, which is a true reflection of the classification algorithm and performance element. 

If you've selected the most accurate predictor variable or independent variable, then there will be a highly stable solution. However, if you've selected a predictor variable that does not accurately with the response variable, then the stability of the solution will not be very accurate.

The reasons as to why an individual might be loyal to the services of a telecommunication company or might opt for another telecommunication company are the things that revolve around the subscription rate fee, for example. And the speed of offering services. We look at things that are in relation to the company itself, and the financial relations of individuals that are subscribed to services of the company. However, when you look at different things like age and gender those are not very important. When you choose the best variable, you will definitely get a highly stable solution.

I've never tackled very, very big datasets with this solution, in the way I've tackled them with other data science software. However, from what I know, it is that it can highly scalable, and can perfectly handle very big datasets without any complications.

A very big dataset is a dataset that has, for example, more than 100,000 rows, or rows that run into the millions.

The technical support is highly responsive. The university developed a 360-degree problem-solving platform. You find it entirely manageable, and interactive. Alongside that, if by any chance you're stuck at any point, then someone will get back to you via email or live chat. They're, highly interactive, and of course, they're there. It's why this is a very, very good platform.

I have experience with solutions like R, Bacillus, and Python for data science. Python, in itself, has the best visualization ever. You can clearly see graphs nicely floated, normal distributions perfectly done, key distribution perfectly done, perfectly elaborated and perfectly labeled. 

This is different from Weka, as, when you want to visualize everything all at once, you'll get a tiny graph. With Python, you can visualize all the graphs, and it doesn't matter whether they have picked your 100. All you have to do is change the scale of the graph, and then you will have a longer chart, but with highly defined graphs. If you want to visualize one particular graph, then the visualization will also be clear. 

A pro on the side of Weka is that you do not need to have programming skills. With Weka, you just point as you grow, as you change, as you drag, as you drop, and as you click and you just run, and things show up. If you are a data scientist or you are a data analyst and you don't have enough coding tiers, then Weka is the right tool to use. But if you are good at coding, then you can go to Python.

The process is straightforward. They did excellent work in easing up the processes of actually installing Weka. They also have tutorials on different platforms that make it easier for one individual to make references, go back and forth, and clear errors. 

The data and the specifications that I've set up, when running any machine learning algorithm using Weka, are easy. For instance, let's say, I have a dataset with 400 rows, and then I have another dataset with maybe 50,000 rows. If I am to classify a specific variable which has yes or no entry, and the one with 400 rows, when I'm using folds, it will be classified faster. Then, with the one with the 50,000 rows, it will be classified slower as we have to do 10 fold for maybe 50,000 rows. It really depends on the size of the dataset how long it takes.

I use both the paid and the open-source versions of the product. If you're a client and you don't want very many details incorporated in your solution, then we will go full open-source. Open source doesn't have very many solution alignment incorporations. However, the paid version has very many options and stuff that needs to be incorporated when providing a solution. It depends on the specifications of a client which we would use. It's not about the price.

The solution is a desktop application. I did not deploy it on the cloud, actually. It's an application that is on my desktop, on my laptop.

If they want their task done faster, and they do not have enough coding expertise, this is definitely an excellent solution to choose from. If they want additional experience because Python and R might be a good option. With Weka, it looks like you're using maybe something like a Microsoft power BI. With Python or R you're actually giving a data scientist a run for his money as things change every day and things evolve and you have to dig deeper, you have to provide new stuff. 

Overall, I'd rate the solution nine out of ten. It's tied with R in terms of how I would rate it. However, I find Python the best.

I mainly use Weka to check data for anomalies.

Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data.

Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science.

I've been using Weka for less than a month.

Weka is stable.

Weka is not scalable.

The initial setup was easy and took around five to ten minutes.

I would rate Weka eight out of ten.

My domain is pure data analysis and data science machine learning. The first time I used Weka, five years back, I did a research project. I prefer to work with Weka whenever I have small and clear projects.

Weka is a very nice tool and it helped me to solve any machine learning problem in one minute. In case of machine learning algorithms, classification, or support machines, I used to use this tool to implement those algorithms. 

Whenever I get any work on any other platform suppose in hours. So what I initially do, I ran the data set in the Weka platform first. It gives me a clear view that this data set has certain attributes and offers some observations. I can implement different machine learning algorithms if this is a classification.

I use two or three algorithms. If we find that the performance of the logistic regression is good then I can implement those in other platforms also.  Weka is a good tool for any analysis. 

There are some missing values there. We can replace the missing values using the mean values. I use that filter to see which names were replaced. It's in the filter, then we have to go to that unsupervised, then replace missing values.

I use that filter to replace missing data. Weka has the option to check important attributes. I use that internally, I found that everything is important. Then initially I applied my dataset to implement the classification problem. 

There is less demand for projects that require Weka as opposed to R or Python.

Weka is a very nice tool, it needs very small requirements. If I want to implement something in Python, I need a lot of memory and space but Weka is very lightweight. Anyone can implement any kind of algorithm, and we can show the results immediately to the client using the one-page feature. The client always wants to know the story. They want the result. 

Accuracy is 90% and we can show the predicted results. What the predicted error is and what the true positive rate is. This is what the user wants. They don't want to know how complex your queries are. They don't bother about it.

It saves time for the developer and provides a summarized view without unnecessary information for the customer. 

Performance is one of the most valuable features. I can plug in any machine learning algorithm and it works perfectly. It is very easy to use the filter. Weka has a good number of filter options. If I compare it with other platforms, I can just use a filter with Weka and apply it. If I want to just convert the numeric data into categorical data, is only a one minute job. We just use a filter and it works. In other platforms, I need to write at least four or five lines of code and I have to check the data.

I'm very comfortable with Weka, for these kinds of things, especially their filter and their Classification Algorithm. They have a good number of algorithms. If I want to do this kind of thing with Python, it will take 20 lines of code minimum.

If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated. You will be lucky enough if you get clean data. Every time we get this kind of data with missing values, if we try to understand how many missing datasets there are if it is very less, we just remove this from the dataset itself before importing that. 

There is no use of algorithm pipelines. In Python, we create a pipeline. First, we use that kind of clustering algorithm, suppose K means clustering, based on that specific cluster, we can choose one cluster. And based on that cluster, we can implement an algorithm. This pipeline is missing in Weka. 

There is also a problem with the visualization. It only can do only two or three types of visualizations.

The solution is stable. There are no problems. Weka is perfect, there is no problem with Weka. 

They have a good set of machine learning algorithms. There aren't any problems with building algorithms.  

The best thing is that it always gives the result in one output page, it contains which algorithm I have implemented, the accuracy of the model, the matrix, and the output of that specific classifier. It gives everything. As a single machine learning tool, Weka is a very good tool.

The initial setup is very easy. If we get a medium set of data and it's clear data, and if we get the tiered instruction, then Weka is the best tool. For simple analysis, I use Weka to first implement different kinds of models in Weka. If logistic regression gives me good prediction and accuracy, I implement this in R or Python. I use Weka as my first tool.

The time it takes to implement depends on the data set. If I know the data inspection, data cleaning, data implementation model, choose the best model, get the accuracy, it will take around a minimum of two hours.

Weka is a very simple tool and it has built-in algorithms, which we need do not to implement. It gives concise results that we can display to our clients. Weka is also a very useful tool for filtering. There are a set of built-in filters that we can use to filter our data.

If you want to take a sample set of data, suppose a specific percentage of data, or if we want to convert a specific data type to another data type, Weka has good filtering features. We can also use cluster and association rules in Weka. These are the advantages of Weka. If I compared this with R and Python, both can do things better than Weka. There is no doubt. But it is not easy to implement algorithms in R and Python, you need at least 20 lines of code and you need a specific setup. You need a specific setup. You need to import the data set. You need to use a different kind of package. With respect to Weka, those are a bit complex. Weka cannot use its visualization power. 

I would rate Weka a six out of ten. The visualization and statistical analysis need improvement.

I used Weka for my Master's thesis. I've used it a couple of times for my personal usage or a quick analysis or graph. You can do a reselection quicker and you can get the graph and put it in our report and do classification. If any project is present, I could develop it.

For my Master's thesis, I could do a quick analysis. We have a huge amount of data. It was not big data, but it was many Giga. I had to work with it in batches, but it helped me to apply prediction models to do some hypotheses with the data to analyze the reason why some people were migrating, depopulating the rural towns, and going to the peripheral grounds around the big cities. In this case, I used many tools like Access, Weka, and some other GIS (Geographic Information Services) for mapping. But Weka helped me with this analysis in an easy way. I could select and apply models. It helped me to do it faster.

There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way. You have to do it in Python and you have to write some lines but in this case, it's in data pre-processing. For the model evaluation, you can build models for classification. 

The product is good, but I would like it to work with big data. I know it has a Spark integration they could use to do analysis in clusters, but it's not so clear how to use it. In this case, it would be more how to handle big amounts of data. My project in my thesis was not so big. It was not 100 Gigabytes, but for sure these tools could be really useful. They should integrate it in a better way with Spark and have better cluster processing.

I have been using Weka for one year. 

It crashes sometimes, but in this case, I think the product is good. You have to have a good computer to do a better analysis. I think it's stable but I think it can grow. It's good, but it's not the past product they have used for data mining, but it can give good results. You have to have patience, you have to take care of your memory, and take care of your CPU resources. It could improve its performance but it is stable.

I have not exported Python and then used it in a Kubernetes application. In that way, scalability is not so good. It should improve. With the amount of data it can process without crashing, it has to work better with other plugins or integration with other tools. The scalability is not so good.

We don't use their official support but we use their forums. There's a lack of information for specific points, but in general, I could find their answer to 80% of my questions.

I have previously used Knime and Orange. Knime has better reviews. Weka has been used but it did not add too many features in the past few years. I think Weka and Orange are a bit stuck. Knime has grown a lot by adding more plugins, adding more capabilities for machine learning, and adding more algorithms. 

Knime is more spread out and has better functionalities. 

The initial setup was straightforward. It is very intuitive and the tool is easy to use. There is some previous knowledge you have to have because you have to know some of the parameters are and what the influence on the final model is.

Weka is good to start in data mining. The base had to be clear with base concepts about the models or algorithms you are going to use. You want to test or do some research first. But for production, it's not the best option. It would be a good tool for prototyping. Knime is the best tool for data mining. 

Weka is good for structured table data. You can use many supervised or unsupervised algorithms, but it's very difficult to get interpretable results about the multilayer option it has. It's not so easy to understand the neural networks if you work with Weka. It would be better to work with unsupervised algorithms like tree-based or clustering algorithms, but not for neural networks. There are other tools that can be more useful.

I would rate Weka a seven out of ten.