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Correct Answers

%Correct Answers

**Explanation:**
The Radom Forest algorithm builds an ensemble of Decision Trees, mostly trained with the bagging method.

**Explanation:**
The gradient of a multivariable function at a maximum point will be the zero vector of the function, which is the single greatest value that the function can achieve.

**Explanation:**
The model performance assessment for classification algorithms encorporates all of the above techniques.

**Explanation:**
A good test dataset has a good amount of sample population and equal ratios of class representation.

**Explanation:**
Allowing a decision tree to split to a granular degree makes decision trees prone to learning every point extremely well to the point of perfect classification that is overfitting.

**Explanation:**
All of the above techniques are different ways of imputing the missing values.

**Explanation:**
Cross-validation is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set.

**Explanation:**
If the second-order difference is positive, the time series will curve upward and if it is negative, the time series will curve downward at that time.

**Explanation:**
You need to always normalize the data first. If not, PCA or other techniques that are used to reduce dimensions will give different results.

**Explanation:**
All of the above techniques transform raw data into features which can be used as inputs to machine learning algorithms.

**Explanation:**
pca.components_ is the set of all eigen vectors for the projection space.

**Explanation:**
Naive Bayes assumes that all the features in a data set are equally important and independent.

**Explanation:**
A large value results in a large regularization penalty and therefore, a strong preference for simpler models, which can underfit the data.

**Explanation:**
K-Means clustering algorithm has the drawback of converging at local minima which can be prevented by using multiple radom initializations.

**Explanation:**
Lemmatization and stemming are the techniques of keyword normalization.

**Explanation:**
K-means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different, and the data points follow nonconvex shapes.

**Explanation:**
This will maintain the structure of the data and also reduce its dimension.

**Explanation:**
You need the gradient descent to quickly converge to the minimum. So the current setting of a seems to be good.

**Explanation:**
Sentence parsers analyze a sentence and automatically build a syntax tree.

**Explanation:**
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