Technology Vocabulary

#421

Decision tree

Example sentences

1. A decision tree makes predictions through a series of questions.
2. The decision tree checked income, age, and payment history.
3. A simple decision tree is often easy to explain.

#

#422

Random forest

Example sentences

1. A random forest combines many decision trees.
2. The random forest gave a more stable result than one tree.
3. The team used a random forest to identify risky transactions.

#

#423

Support vector machine

Example sentences

1. A support vector machine separates groups by finding a clear boundary.
2. The team tested a support vector machine for text classification.
3. A support vector machine can work well with carefully prepared data.

#

#424

K-means

Example sentences

1. K-means divides data into a chosen number of groups.
2. The analyst used K-means to create three customer groups.
3. K-means moves each group center until the result becomes stable.

#

#425

Gradient descent

Example sentences

1. Gradient descent changes model values step by step to reduce error.
2. The model improved as gradient descent continued.
3. A very large step can make gradient descent miss a good solution.

#

#426

Loss function

Example sentences

1. The loss function measures how wrong a model's predictions are.
2. Training tries to reduce the loss function.
3. The team chose a loss function that matched the task.

#

#427

Accuracy

Example sentences

1. Accuracy is the percentage of predictions that are correct.
2. The model reached ninety percent accuracy.
3. Accuracy alone may be misleading when one group is much larger than another.

#

#428

Precision

Example sentences

1. Precision measures how many positive predictions were actually correct.
2. High precision is important when false alarms are costly.
3. The model's precision improved after the team changed the decision level.

#

#429

Recall

Example sentences

1. Recall measures how many real positive cases the model found.
2. The medical test needs high recall so it misses fewer sick patients.
3. Increasing recall may sometimes reduce precision.

#

#430

F1 score

Example sentences

1. The F1 score balances precision and recall in one value.
2. The team compared models by their F1 score.
3. A higher F1 score showed better balance between the two measures.

#

#431

Confusion matrix

Example sentences

1. A confusion matrix shows correct and incorrect predictions by category.
2. The confusion matrix revealed that two classes were often mixed up.
3. The team studied the confusion matrix before changing the model.

#

#432

Overfitting

Example sentences

1. Overfitting happens when a model learns the training data too closely.
2. The model showed overfitting because it failed on new examples.
3. More varied data can help reduce overfitting.

#

#433

Underfitting

Example sentences

1. Underfitting happens when a model is too simple to learn useful patterns.
2. Low performance on both training and test data may show underfitting.
3. The team added more useful features to reduce underfitting.

#

#434

Bias

Example sentences

1. Bias is a consistent error that pushes results in one direction.
2. Biased training data can create bias in a model.
3. The team checked the system for unfair bias.

#

#435

Variance

Example sentences

1. Variance describes how much a model changes with different training data.
2. A very complex model may have high variance.
3. The team reduced variance by using more data.

#

#436

Hyperparameter

Example sentences

1. A hyperparameter is a setting chosen before or during model training.
2. The learning rate is an important hyperparameter.
3. The team tested several values for each hyperparameter.

#

#437

Natural language processing

Example sentences

1. Natural language processing helps computers understand and produce human language.
2. The chatbot uses natural language processing to read questions.
3. Translation tools are a common use of natural language processing.

#

#438

Computer vision

Example sentences

1. Computer vision helps machines understand images and video.
2. The factory uses computer vision to find damaged products.
3. Computer vision allowed the car to recognize a road sign.

#

#439

Speech recognition

Example sentences

1. Speech recognition changes spoken words into text or commands.
2. The phone uses speech recognition when I speak a message.
3. Background noise can reduce speech recognition quality.

#

#440

Generative AI

Example sentences

1. Generative AI creates new text, images, audio, or other content.
2. The designer used generative AI to explore several ideas.
3. People should check generative AI output for errors.

#