Which area of machine learning is concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward?
Anonymous Quiz
25%
Supervised Learning
67%
Reinforcement Learning
8%
Unsupervised Learning
What is gradient boosting trees?
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
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Which of the following is a supervised Learning technique?
Anonymous Quiz
18%
Clustering
75%
Classification
7%
Association
Which training set is provided for supervised learning?
Anonymous Quiz
19%
Only inputs
5%
Only outputs
76%
inputs and correct outputs
Which of the following is correct for unsupervised learning?
Anonymous Quiz
11%
models are supervised using training dataset
74%
models itself find the hidden patterns and insights from the given data
15%
models cannot itself find the hidden patterns and insights from the given data
Which type of problems can be solved with Unsupervised learning?
Anonymous Quiz
15%
Classification problems
76%
Customer segmentation (Clustering)
9%
Linear Regression
Supervised learning requires a training set to teach models to yield the desired output. Training dataset includes inputs and correct outputs, which allow the desired model to learn over time. The algorithm also measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.
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What is the ROC curve? When to use it?
ROC stands for Receiver Operating Characteristics. The diagrammatic representation that shows the contrast between true positive rate vs false positive rate.
It is used when we need to predict the probability of the binary outcome.
ROC stands for Receiver Operating Characteristics. The diagrammatic representation that shows the contrast between true positive rate vs false positive rate.
It is used when we need to predict the probability of the binary outcome.
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What’s the interpretation of the bias term in linear models?
Bias is simply, a difference between predicted value and actual/true value. It can be interpreted as the distance from the average prediction and true value i.e. true value minus mean(predictions). But dont get confused between accuracy and bias.
Bias is simply, a difference between predicted value and actual/true value. It can be interpreted as the distance from the average prediction and true value i.e. true value minus mean(predictions). But dont get confused between accuracy and bias.
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Data scientists often tune bias values to
Anonymous Quiz
10%
Reset the inputs
50%
better fit the data
33%
Improve the quality of data
6%
None of the above
What kind of problems neural nets can solve?
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
What is AUC (AU ROC)? When to use it?
AUC stands for Area Under the ROC Curve. ROC is a probability curve and AUC represents degree or measure of separability. It's used when we need to value how much model is capable of distinguishing between classes. The value is between 0 and 1, the higher the better.
AUC stands for Area Under the ROC Curve. ROC is a probability curve and AUC represents degree or measure of separability. It's used when we need to value how much model is capable of distinguishing between classes. The value is between 0 and 1, the higher the better.
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What is the PR (precision-recall) curve?
A precision-recall curve (or PR Curve) is a plot of the precision (y-axis) and the recall (x-axis) for different probability thresholds. Precision-recall curves (PR curves) are recommended for highly skewed domains where ROC curves may provide an excessively optimistic view of the performance.
A precision-recall curve (or PR Curve) is a plot of the precision (y-axis) and the recall (x-axis) for different probability thresholds. Precision-recall curves (PR curves) are recommended for highly skewed domains where ROC curves may provide an excessively optimistic view of the performance.
What is the area under the PR curve? Is it a useful metric?
The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score.
A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score.
A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
What do we do with categorical variables?
Categorical variables must be encoded before they can be used as features to train a machine learning model. There are various encoding techniques, including:
One-hot encoding
Label encoding
Ordinal encoding
Target encoding
Categorical variables must be encoded before they can be used as features to train a machine learning model. There are various encoding techniques, including:
One-hot encoding
Label encoding
Ordinal encoding
Target encoding
Data Science & Machine Learning
Which algorithm builds one tree at a time?
What’s the difference between random forest and gradient boosting?
Random Forests builds each tree independently while Gradient Boosting builds one tree at a time.
Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.
Random Forests builds each tree independently while Gradient Boosting builds one tree at a time.
Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.
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What happens to our linear regression model if we have three columns in our data: x, y, z — and z is a sum of x and y?
We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.
We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.
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Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more.
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Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more.
https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074?
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