It totally depends on what sort of feature engineering you use. Except for the case of KNN which is useless. Naive Bayes will work well with Bag of Words and TF-IDF while Logistic regression will perform well on all including Word2Vec.
n_neighbors is the number of neighbors to take into account. This value is chosen and set by you when you program the KNN model. You can test different KNNs with different values for it, and it is called an hyperparameter. You can use GridSearchCV to test for different values of it.
Then the KNN model will look for the closest n_neighbors (of the new data ...
It depends which scenario you chose.
When you train any data science model, it won't move anymore.
For example, if you train K-Means, you'll get at a result the cendroid of every cluster. If you train a random forest, you'll have as a result your trees.
Then, when you apply your model, it gives you an answer according to that. The answer will always be the ...
This question needs to be more specific. And there might be confusion.
n_neighbors, is the number of proximity neighbors that the algorithm uses.
metric, is how you define what is the closes neighbor, by default, is Euclidean.
the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to ...
Interaction effect means the target is dependent on the interaction of two features i.e. X,Y but the data doesn't consider that Feature i.e. XY.
Simple model which tries to find a global pattern with the dataset will suffer from this issue e.g. LinearRegression.
Interaction Effect needs a full chapter for itself. Read here
Let's check this plot where the ...
weights = 'distance' is in contrast to the default which is weights = 'uniform'. When weights are uniform, a simple majority vote of the nearest neighbors is used to assign cluster membership.
When weights are distance weighted, the voting is proportional to the distance value. Nearby points will have a greater influence than more distance points (even if ...
My two main remarks are:
KNN being a distance based algorithm, scaling is a must!
Otherwise the distance is distorted by the biggest feature value and small ones are not taken into account properly.
You should try and properly scale or encode all the features.
Could you tell how many features before and after encoding your get?
You may need feature ...