Explained

ENSEMBLE LEARNINGPutting the burden where weak learners need it mostEveryone makes mistakes — even the only decision trees in machine learning. As a substitute of ignoring them, AdaBoost (Adaptive Boosting) algorithm does something different:...

DATA PREPROCESSINGArtificially generating and deleting data for the greater goodCollecting a dataset where each class has the exact same number of sophistication to predict could be a challenge. In point of fact, things are...

CLASSIFICATION ALGORITHMBell-shaped assumptions for higher predictions⛳️ More CLASSIFICATION ALGORITHM, explained: · Dummy Classifier · K Nearest Neighbor Classifier · Bernoulli Naive Bayes ▶ Gaussian Naive Bayes · Decision Tree Classifier · Logistic Regression ·...

DATA PREPROCESSINGSix ways of matchmaking categories and numbers10 min read·19 hours agoAh, categorical data — the colourful characters in our datasets that machines just can’t seem to grasp. That is where “red” becomes 1,...

DATA PREPROCESSINGOne (tiny) dataset, six imputation methods?Let’s discuss something that each data scientist, analyst, or curious number-cruncher has to take care of in the end: missing values. Now, I do know what you’re considering...

Fastest implementation in python🐍That’s it, they're similar. 5 minutes and we’re done! Whenever you try DBSCANning yourself, don’t forget to tune epsilon and the variety of neighbors since they highlt influence the ultimate results.===========================================Reference:...

Unlocking Predictive Power Through Binary SimplicityLike several algorithm in machine learning, Bernoulli Naive Bayes has its strengths and limitations.Simplicity: Easy to implement and understand.Efficiency: Fast to coach and predict, works well with large feature...

The friendly neighbor approach to machine learninglabels, predictions, accuracies = list(y_test), , k_list = for k in k_list:knn_clf = KNeighborsClassifier(n_neighbors=k)knn_clf.fit(X_train, y_train)y_pred = knn_clf.predict(X_test)predictions.append(list(y_pred))accuracies.append(accuracy_score(y_test, y_pred).round(4)*100)df_predictions = pd.DataFrame({'Label': labels})for k, pred in zip(k_list, predictions):df_predictions = preddf_accuracies...