Language
This challenge occurs when a word, phrase, or sentence can have multiple possible interpretations.
What is Ambiguity?
This type of machine learning model creates new content similar to its training data.
What is Generative Model
An operation that converts data and objects, such as text, into a numerical representation.
In machine learning, this carefully reserved portion of data serves as the final, unseen benchmark to assess how well a model generalizes beyond its training examples.
What is Test-Set?
This evaluation metric measures the proportion of actual positive instances that a machine learning model correctly identified.
This linguistic phenomenon in NLP refers to the many ways humans can express the same core meaning using different words, sentence structures, or grammatical variations.
What is Variability?
This predictive modeling technique uses a graph of binary questions and their possible consequences, resembling a flowchart that breaks down complex choices.
What is Decision Tree?
This simple text representation model converts a document into an unordered set of tokens, ignoring grammar and order while tracking their frequencies.
What is "Bag of words"?
In this problematic scenario, a model performs exceptionally well on training data but fails dramatically when presented with new, independent datasets.
What is Overfitting?
This performance metric measures the proportion of positive predictions that are also correct.
What is Precision?
This linguistic law states that the frequency of any word is inversely proportional to its rank in the frequency table of all words in a language.
What is Zipf law?
This advanced ensemble method combines multiple decision trees to create a more robust and accurate predictive model, reducing overfitting and improving generalization.
What is Random Forest?
This preprocessing step in bag-of-words often involves removing common words like "the", "a", "I", and converting all text to lowercase to reduce dimensionality and computational complexity.
What is Normalization & Stopword Removal?
This fundamental machine learning concept describes the balance between a model's tendency to oversimplify the learned patterns, and its sensitivity to fluctuations and fine changes in training data.
What is the Bias-Variance Tradeoff?
This metric balances the trade-off between identifying all relevant instances and maintaining high accuracy.
What is F1-Score?
In NLP, these are the unique words or vocabulary items in a text corpus.
What are Types?
this classification method calculates the probability of a data point belonging to a particular class by multiplying individual feature probabilities.
What is Naive Bayes?
An advanced variation for text representation that weights word importance by comparing word occurrence count to its count in other documents in the corpus.
What is TF-IDF?
This process involves adjusting the configuration settings of a machine learning model that are not learned from the data itself, but are set before training begins.
What is Hyperparameter Tuning?
This metric measures the quality of binary and multi-class classifications and works effectively even with imbalanced datasets.
What is the Matthews Correlation Coefficient (MCC)?
This phenomenon in linguistics and NLP refers to the use of language to narrow down or limit the set of possible referents or meanings, often seen with modifiers like adjectives or negations.
What is Restrictivity?
This conceptual line or surface in feature space separates different class predictions in a classification model.
What is Decision Boundary?
A text representation approach that captures semantic relationships between words and contextual nuances.
What are Word Embeddings?
This dataset subset is used during model training to provide an intermediate performance evaluation and help prevent overfitting.
What is Validation Set?
Techniques that use multiple iterations of this dataset to provide a more robust and statistically reliable assessment of model performance.
What is Cross-Validation?