Why does reducing the n_estimators in RandomForestClassifier improve accuracy? I am taking a course that introduced me to sklearn.ensemble.RandomForestClassifier. At first it uses n_estimators with the default value of 10 and the resulting accuracy turns out to be around 0.28. If I change n_estimators to 15, the accuracy goes to 0.32
Here's some of the code:
pl = Pipeline([
        ('union', FeatureUnion(
            transformer_list = [
                ('numeric_features', Pipeline([
                    ('selector', get_numeric_data),
                    ('imputer', Imputer())
                ])),
                ('text_features', Pipeline([
                    ('selector', get_text_data),
                    ('vectorizer', CountVectorizer())
                ]))
             ]
        )),
        ('clf', RandomForestClassifier())
    ])

I thought that increasing the number of trees (n_estimators) in the RandomForestClassifier would give a better accuracy, but sometimes if I use a value of 100 I can get between 0.30 and 0.32. Could someone please explain? How do you find which is the smallest value for getting the highest possible accuracy?
If you are talking about testing accuracy in this case (ie you are comparing results on data you didn't train with) - it's possible that adding more estimators is overfitting on your training set and is therefore performing poorly on your holdout set. If this is the case I would recommend approaching the problem with a more basic method such as LogisticRegression - as it is less likely to overfit when compared to ensemble methods.
As for finding the best parameters - try sklearn's RandomizedSearchCV to fine-tune your hyperparameters to maximize performance.