# Week8

## Types of Data for classificatin

### Qualitative Data

* Categorical or nominal data
* Ordinal or Ranked Data

  **Quantitative Data**
* Discrete Data
* Continuous measurements

## Handling Categorical/Nominal data----dummy encoding

```python
from statsmodels.tools import categorical
cat_encod=categorical(data, dictnames=False, drop=True)
# dictnames: create a dict
# drop: create new /drop data
```

## Support Vector Machine (SVM)

<https://oceandatamining.sciencesconf.org/data/program/OBIDAM14_Canu.pdf> <https://web.stanford.edu/~hastie/Papers/ESLII.pdf>

```python
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=f)
from sklearn.svm import SVC
svc=SVC(c=1.0, kernel='rbf', degree=3,gamma='auto',probability=Fales, tol=0.001, max_iter=-1, random_state=None)
svc.fit(x_train, y_train)
svc.predict(x_test)
svc.predict(x_train)
# c: penalty parameter
#kernel: 'rbf','sigmoid','poly'
#tol: stop criterion
#max_iter: -1 no limit;
#random_state: random-seed to use
```

## Naive Bayesian

A collection of classification algorithms.

```python
from sklearn.naive_bayes import GaussianNB
NB =GaussianNB(priors)
NB.fit(X,y)
NB.predict(X_dash)
```

## Random Forest

```python
from sklearn.ensemble import RandomForestClassifier
RF = RandomForestClassifier (n_estimators =10, random_state=12)
RF.fit(x,y)
RF.predict(x_dash)
```

## Classification Metrics: confusion matrix!

Diagonal elements of the matrix, it contains the number of correctly identified samples for each class

```python
from sklean.metrics import confusion_matrix
CM=confusion_matrix(y_actual, y_predicted)
print(CM)
```

important note: use heatmap to plot the CM

```python
plt.subplot(131)
sns.heatmap(CM.T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label')
```

## Exercise

### RF

Import the dataset from the following ‘url’ and do a classification with decision tree and Random Forest (RF) with number of trees equal to 5, and compare the result of testing data with confusion matrix.

```python
import seaborn as sns
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from statsmodels.tools import categorical
from sklearn import tree
# ------------------ Loading Dataset --------------------------#
dataframe = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-
 databases/abalone/abalone.data")
dataframe = dataframe .drop(dataframe .index[:-1000])
numericdf = dataframe[dataframe .columns[1:9]]
categordf = dataframe[dataframe .columns[0]]
categordf_en = categorical(categordf.values , drop=True)
categordf_en = categordf_en[:, 0:2]
numeric_arr = np.asarray(numericdf.values)
categor_arr = np.asarray(categordf_en)
Output = numeric_arr[:, 7]
Input_numeric = numeric_arr[:, 0:6]
Input_categor = categor_arr
Input = np.concatenate((Input_numeric, Input_categor), axis=1)
#---------------------------------------------------------------#
RF = RandomForestClassifier(n_estimators=5, random_state=12)
RF.fit(Input, Output)
Z_RF = RF.predict(Input)
CM_RF= confusion_matrix(Output, Z_RF)
#---------------------------------------------------------------
DT = tree.DecisionTreeClassifier()
DT.fit(Input, Output)
Z_DT = DT.predict(Input)
CM_DT= confusion_matrix(Output, Z_DT)
#---------------------------------------------------------------
plt.subplot(121)
sns.heatmap(CM_RF.T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.subplot(122)
sns.heatmap(CM_DT.T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.show()
```


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