I've read and heard about the mighty XGBoost which is one of the most famous models people are using today to solve Kaggle challenge. This makes me interest to develop my own intuition about the model so I decided to try XGBoost on my dataset, but unfortunately, I experienced a lot of dependency related problems when I tried to install XGBoost on mac :(However, I did a bit more researches and found out another related library called LightGBM from Microsoft which they claim to achieve better result (or at least, equivalent) over XGBoost. Therefore, I decided to go with the LightGBM instead since I didn't encounter any problems during the library installation.I expected that the model should outperform, or at least, perform similarly compared to other models on the same dataset. My dataset contains 14k of text documents and has 0 or 1 values which refer to positive and negative sentiment respectively as target variable.As a result showing below, LightGBM with count vectorizer as input got the 6th place with an accuracy of 94.60%. I'm quite curious since the accuracy of LightGBM is not only lower than SVC but also lower than Extra Tree model (300 esitimators). I don't have enough foundation and solid understanding behind these models so I couldn't come up with an answer about these outcomes. Can someone please give me an idea or assumption according to the result?PS. All models was performed with 5-fold cross validation model             score--------------  -------extraT           0.9528svc              0.9514sgd_elas         0.9481extraT_tfidf     0.9476svc_tfidf        0.9473lightGBM         0.9460sgd_elas_tfidf   0.9458lightGBM_tfidf   0.9420randomF          0.9409randomF_tfidf    0.9345mulNB            0.9307berNB            0.9087berNB_tfidf      0.9087mulNB_tfidf      0.9036
Why doesn't LightGBM perform better than SVC (linear kernel)? [sentiment analysis]
machine learning;svm;xgboost