scikit-learn Review

PROS: Software installation is pretty easy. Importing the library and using the ML models is also a breeze. But the best thing is, it offers so many ML learning models we can use including xgboost and random forest. The models can be modified, which is really helpful. Also, we don’t have to code from scratch, which saves us a significant amount of time.

CONS: It is slow compared to tensorflow. Also, modifying the machine learning models can be pretty challenging.

PROS: I can use this to do a lot of things like pre processing, regression, clustering and classification. If you are working on ML based research, I suggest you give this a try. I promise it's worth it.

CONS: No cons. I find this package really amazing, reliable, and efficient. You'll just need basic Python coding knowledge to be able to maximize its value.

PROS: It contains a wide range of perfectly coded machine learning algorithms which makes it easier to accomplish ML related tasks. No need to code the algorithm from scratch so we are able to save a lot of time. Users can focus on the application itself and worry less about the implementation. Also, it offers a huge variety of dataset which can be utilized for testing. Scikit-learn has so much to offer. So it's not surprising that many open source developers are supporting it and a lot of individuals have been using this library.

CONS: Nothing. I definitely love this library. In fact, I usually spend almost all my working hours using this.

PROS: I can access a wide range of machine learning algorithms such as logistic regression, linear regression, etc. and use them on our dataset through a single line of code. I train the model on our dataset and utilize that model to foresee further values. It also allows me to change the algorithm’s parameters according to the usage and save the trained model.

CONS: I don’t see any drawback. Scikit-learn is just your classic machine learning library for Python.

PROS: I’m a satisfied user of this tool for many years now and I think the best thing about it is that it makes it easier to implement and use machine learning algorithms. I started using this tool when I was still a student and I am still using it until now. It helps me complete ML tasks faster. Another thing, scikit-learn is becoming better and better and its algorithms remain up-to-date.

CONS: Scikit-learn is a decent library and through the years of using it, I still haven’t seen any disadvantage.

PROS: We're able to train data easily, and develop classifier and prepare regression models faster. But I think the best thing about using scikit-learn is the ability to save our model and trained data for future references.

CONS: Nothing. This machine learning library is generally awesome.

PROS: Perfect for those with experience in machine learning but have little knowledge about the implementation. It allows users to make any regression or classifier model by simply calling its object and use the training set to train the object. Afterwards, the ready-trained model will be capable of predicting further results. Modifying the parameters of a certain algorithm is also simple. Just call the object and pass the needed values. Lastly, scikit-learn got a nice documentation that you can understand easily.

CONS: I believe using this library brings no disadvantage.

PROS: scikit-learn is by far the best open source machine learning library for Python in the market today. It is simple yet powerful. We are able to access almost all ML algorithms and everything is perfectly coded so we don't have to. The algorithms can be used easily through a single line of code. The parameters can be customized according to your requirements. You can generate models with ease and use them to predict values.

CONS: None. Scikit-learn is an amazing library.

PROS: It’s our go-to-tool for most of our standard machine learning tasks such as regression, dimensionality reduction, clustering, classification, etc. It’s efficient and great for beginners.

CONS: Statistics is lacking. It would be nice if they give more focus on this. For instance, provide more details about regression. Some of scikit-learn competitors do better in this aspect.

PROS: Its interface is great and the module is up-to-date. It got many different algorithms which makes it easier to solve complicated problems. Overall, the platform is easy to use, scalable and robust.

CONS: If there is a high need for stat information, it cannot be likely used.

PROS: With lots of tools it has to offer, it's possible to use this solution to develop an end-to-end machine learning platform. It has a plethora of machine learning algorithms like decision tree, logistic regression, support vector machines, linear discriminant analysis, and other clustering algorithms as well as boosting algorithms. There are also available pre-processing tools as well as hyperparameter tuning tools including GrindSearchCV and RandomSearchCV. It also offers different metric types to tune the model for precision, accuracy, etc. On top of that, this software works well with motplotlib, pandas, and other Python libraries.

CONS: It doesn't contain more advanced algoritms like LightGBM, XGBoost, and Catboost. Also, hyperparameter tuning is quite time consuming. Facilitating GPU may solve this problem.

PROS: It lets you train the ML models and gives nice and easy-to-understand documentations. Getting started with Machine Learning can be quite scary and overwhelming but scikit-learn makes things easier and simpler.

CONS: I’ve been using scikit-learn for a while now and everything is still great and working perfectly. No drawback.

PROS: It is easy to create and run ML algorithm for any model. Whenever I need to utilize a linear regression model, I simply call its object, train my data on it, and predict when needed. Whenever I need a KNN for face recognition I simply call its KNN classifier with the right hyper parameters and utilize it in the face recognition model. It’s very simple. There are also several custom datasets available which can be easily imported and used. Overall, simplicity is the best thing about this tool.

CONS: I haven’t found any cons yet. Scikit-learn is a great library.

PROS: We use its support vector classifier in mapping ultrasound imagery to hand movements and it works fairly well. Generally, the platform is very simple which makes it ideal for begginners but it is also extremely scalable so you can use it to do more tasks.

CONS: Poor documentation but other than that scikit-learn is very consistent and reliable.

PROS: Almost all machine learning algorithms are available in this library. Its documentation is pretty fast, efficient, and great for beginners. Lastly, it is open source. If you are new in machine learning or just looking for a great ML library for Python, I highly recommend this one.

CONS: I use scikit-learn for all my machine learning tasks and by far it is efficient and I haven't encountered any serious concern yet (hopefully, I won't)

PROS: I think its the simplicity that makes scikit-learn awesome and different from other machine learning frameworks. It offers incredible one line function calls to complicated functions. The documentation is nice and human readable that even a begginner would be able to easily understand it. The best thing is, scikit-learn offers libraries for data reprocessing which means you can do a lot of things with it.

CONS: Data should be in a particular format in order to train models. Also, the process is pretty time consuming. And there are times when an error message does not give much insight into what has gone wrong ,which can be really stressful.

PROS: It offers a number of different machine learning algorithms, eliminating the need to build one from scratch. There's also the ability to split the provided dataset into train as well as validation data through passing split ratio. Lastly, it can be integrated with other deep learning frameworks.

CONS: I haven't found any cons of using scikit-learn. It's great and has helped me accomplish many things in machine learning.